Novel Methods For Volterra Filter Representation ...

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Novel Methods For Volterra FilterRepresentation, Identification and

Realization

Ender M. EKSIOGLU, M.Sc.

Advisor: Prof. Ahmet H. KAYRANIstanbul Technical University ? Institute Of Science And Technology

Electronics and Communications Engineering DepartmentFebruary 2005

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Main Headings

� Introduction� Volterra Filters� A Novel Volterra filter Representation� Identification Of The Volterra Kernel Vectors Using

Deterministic Input Signals� Simulations for the System Identification Setting� Some Numerical Properties of the Identification Algorithm� Application to the Identification of Nonlinear Communication

channels� A Novel Volterra Filter Realization Method and its

Application to Nonlinear Adaptive Filtering� Concluding Remarks

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Main Headings

� Introduction

� Volterra Filters� A Novel Volterra filter Representation� Identification Of The Volterra Kernel Vectors Using

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Main Headings

� Introduction� Volterra Filters

� A Novel Volterra filter Representation� Identification Of The Volterra Kernel Vectors Using

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Main Headings

� Introduction� Volterra Filters� A Novel Volterra filter Representation

� Identification Of The Volterra Kernel Vectors UsingDeterministic Input Signals

� Simulations for the System Identification Setting� Some Numerical Properties of the Identification Algorithm� Application to the Identification of Nonlinear Communication

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Main Headings

Deterministic Input Signals

� Simulations for the System Identification Setting� Some Numerical Properties of the Identification Algorithm� Application to the Identification of Nonlinear Communication

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Main Headings

Deterministic Input Signals� Simulations for the System Identification Setting

� Some Numerical Properties of the Identification Algorithm� Application to the Identification of Nonlinear Communication

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Main Headings

Deterministic Input Signals� Simulations for the System Identification Setting� Some Numerical Properties of the Identification Algorithm

� Application to the Identification of Nonlinear Communicationchannels

� A Novel Volterra Filter Realization Method and itsApplication to Nonlinear Adaptive Filtering

� Concluding Remarks

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Main Headings

channels

� A Novel Volterra Filter Realization Method and itsApplication to Nonlinear Adaptive Filtering

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Main Headings

Application to Nonlinear Adaptive Filtering

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Main Headings

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Introduction

In this dissertation we deal with� discrete-time,� finite-order,� time-invariantVolterra filters.

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Introduction

� Firstly, we develop a new representation for the finite-orderVolterra filters. This representation introduces a novelpartitioning of the Volterra kernels.

� Next, we formulate a novel exact identification method forVolterra filters.

� This identification method is based on the novelrepresentation we develop and uses deterministic sequencesconsisting of impulses with distinct levels.

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Introduction

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Introduction

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Introduction

� We know that the unit impulse response is insufficient to fullycharacterize a nonlinear system unlike linear time-invariantsystems.

� The identification method might be considered as a successfulextension of the impulse response of the linear, time-invariantsystems to the realm of nonlinear systems.

� The developed method indeed includes identification usingthe unit impulse response as a subcase when the system underconsideration is a linear system.

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Introduction

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Introduction

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Introduction

To our best knowledge, this method is the first full-scalegeneralization of the impulse response to the finite orderVolterra type nonlinear systems.

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Introduction

� Our identification method is exact.

� Our method calculates each Volterra kernel individually.� Our method calculates directly the Volterra kernels, instead of

calculating first some intermediary representation.� Our method does not introduce and identify any kernels

which are redundant for the regular Volterra filter.� Our method is parsimonious in the number of kernels

identified and in the length of the input sequence utilized toidentify them.

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Introduction

� Our identification method is exact.� Our method calculates each Volterra kernel individually.

� Our method calculates directly the Volterra kernels, instead ofcalculating first some intermediary representation.

� Our method does not introduce and identify any kernelswhich are redundant for the regular Volterra filter.

� Our method is parsimonious in the number of kernelsidentified and in the length of the input sequence utilized toidentify them.

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Introduction

� Our identification method is exact.� Our method calculates each Volterra kernel individually.� Our method calculates directly the Volterra kernels, instead of

calculating first some intermediary representation.

� Our method does not introduce and identify any kernelswhich are redundant for the regular Volterra filter.

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Introduction

which are redundant for the regular Volterra filter.

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Introduction

which are redundant for the regular Volterra filter.� Our method is parsimonious in the number of kernels

identified and in the length of the input sequence utilized toidentify them.

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Introduction

� We show that the input sequence we develop for identificationis persistently exciting for the Volterra filters underconsideration.

� We further prove the equivalence of our identificationalgorithm to the least squares solution formulation.

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Introduction

� We show that the input sequence we develop for identificationis persistently exciting for the Volterra filters underconsideration.

� We further prove the equivalence of our identificationalgorithm to the least squares solution formulation.

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Introduction

� We apply the novel identification method to the identificationof the Volterra kernels of nonlinear communication channelsmodelled as third-order Volterra filters.

� We demonstrate with several simulations that theidentification algorithm can produce better parameterestimates than some most recent algorithms in the literature.

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Introduction

� We apply the novel identification method to the identificationof the Volterra kernels of nonlinear communication channelsmodelled as third-order Volterra filters.

� We demonstrate with several simulations that theidentification algorithm can produce better parameterestimates than some most recent algorithms in the literature.

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Introduction

� A secondary contribution of this dissertation is in the area oforthogonal realizations for Volterra filters.

� We present a novel fully orthogonal structure for therealization of Volterra filters. This structure is based on arecently proposed 2D orthogonal lattice model.

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Introduction

� A secondary contribution of this dissertation is in the area oforthogonal realizations for Volterra filters.

� We present a novel fully orthogonal structure for therealization of Volterra filters. This structure is based on arecently proposed 2D orthogonal lattice model.

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Volterra Filters

� The use of linear system models has been well establishedwith successful applications.

� However, there are still a large number of problems where onehas to resort to nonlinear system models.

� Linear systems are fully described by their impulse response.� There is no such unified framework for the representation of

nonlinear systems. There are various categories for modellingnonlinear systems.

� In this dissertation we will be dealing with nonlinearpolynomial system models based on the Volterra seriesrepresentation.

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Volterra Filters

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Volterra Filters

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Volterra Filters

� Linear systems are fully described by their impulse response.

� There is no such unified framework for the representation ofnonlinear systems. There are various categories for modellingnonlinear systems.

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Volterra Filters

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Volterra Filters

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Volterra Filters

� Volterra filters based on the Volterra series have been anattractive nonlinear system class due to some desiredproperties.

� Volterra filters bear similarities to the well-developed linearsystem theory.

� Volterra filters can approximate a large class of nonlinearsystems with a finite number of coefficients.

� Many real world processes lend themselves to get modellednaturally by polynomial systems.

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Volterra Filters

� Volterra filters based on the Volterra series have been anattractive nonlinear system class due to some desiredproperties.� Volterra filters bear similarities to the well-developed linear

system theory.

� Volterra filters can approximate a large class of nonlinearsystems with a finite number of coefficients.

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Volterra Filters

system theory.� Volterra filters can approximate a large class of nonlinear

systems with a finite number of coefficients.

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Volterra Filters

system theory.� Volterra filters can approximate a large class of nonlinear

systems with a finite number of coefficients.� Many real world processes lend themselves to get modelled

naturally by polynomial systems.

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Volterra Filters - Overview

� In this section we will provide an overview of the Volterraseries representation for nonlinear systems.

� The Taylor series expansion with memory is known as theVolterra series. The naming is due to Vito Volterra, the Italianmathematician who introduced this polynomial series.

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� For a general continuous-time nonlinear system theinput-output relationship is represented by the followinginfinite continuous-time Volterra series integral.

y(t) = b0 +

∫∞

−∞

b1(τ1)x(t − τ1)dτ1

∫∞

−∞

∫∞

−∞

b2(τ1, τ2)x(t − τ1)x(t − τ2)dτ1dτ2 + . . .

∫∞

−∞

· · ·

∫∞

−∞

bM (τ1, τ2, . . . , τM )x(t − τ1)x(t − τ2) · · ·x(t − τM )dτ1dτ2 · · · dτM

+ . . .

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� For a general continuous-time nonlinear system theinput-output relationship is represented by the followinginfinite continuous-time Volterra series integral.

y(t) = b0 +

∫∞

−∞

b1(τ1)x(t − τ1)dτ1

∫∞

−∞

∫∞

−∞

b2(τ1, τ2)x(t − τ1)x(t − τ2)dτ1dτ2 + . . .

∫∞

−∞

· · ·

∫∞

−∞

bM (τ1, τ2, . . . , τM )x(t − τ1)x(t − τ2) · · ·x(t − τM )dτ1dτ2 · · · dτM

+ . . .

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� The equivalent discrete-time Volterra series sum is given asfollows.

y(n) =b0 +

∞∑

i1=−∞

b1(i1)x(n − i1)

∞∑

i1=−∞

∞∑

i2=−∞

b2(i1, i2)x(n − i1)x(n − i2) + . . .

∞∑

i1=−∞

· · ·

∞∑

iM=−∞

bM (i1, i2, . . . , iM )x(n − i1)x(n − i2) · · ·x(n − iM )

+ . . .

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� The equivalent discrete-time Volterra series sum is given asfollows.

y(n) =b0 +

∞∑

i1=−∞

b1(i1)x(n − i1)

∞∑

i1=−∞

∞∑

i2=−∞

b2(i1, i2)x(n − i1)x(n − i2) + . . .

+∞∑

i1=−∞

· · ·

∞∑

iM=−∞

+ . . .

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Truncated Volterra Filters

� The truncated or doubly finite Volterra series is obtained byconfining the infinite summations to finite values.

� The truncated Volterra series is suitable for the modelling of awide variety of nonlinearities encountered in real-life systems.

� In this thesis we will be concerned with discrete-time, causal,finite-memory, time-invariant nonlinear systems described bythe discrete-time, truncated Volterra series expansion.

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� The truncated Volterra series is given as

y(n) =N∑

b1(i1)x(n − i1)

b2(i1, i2)x(n − i1)x(n − i2) + . . .

· · ·

iM=iM−1

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� The truncated Volterra series is given as

y(n) =N∑

b1(i1)x(n − i1)

b2(i1, i2)x(n − i1)x(n − i2) + . . .

· · ·

iM=iM−1

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� This representation is called as the triangular Volterrarepresentation in the literature.

� In this thesis we will be calling this representation as theVolterra filter.

� The corresponding kernels will be called simply as theVolterra kernels to avoid confusion.

� We will use the triangular representation as the starting pointfor our studies to develop novel Volterra representations.

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� The output y(n) in (3) can be rewritten as below.

y(n) = N [x(n)] =M∑

yk(n) (4)

in whichyk(n) = Bk[x(n)] (5)

� N [ · ] represents the nonlinear system under consideration.� Bk[ · ] represents a kth-order Volterra subsystem with an

input-output relationship constituting k summations.

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y(n) = N [x(n)] =M∑

yk(n) (4)

� N [ · ] represents the nonlinear system under consideration.

� Bk[ · ] represents a kth-order Volterra subsystem with aninput-output relationship constituting k summations.

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y(n) = N [x(n)] =M∑

yk(n) (4)

� N [ · ] represents the nonlinear system under consideration.� Bk[ · ] represents a kth-order Volterra subsystem with an

input-output relationship constituting k summations.

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� The input-output relation for each of these subsystems is asindicated below.

yk(n) = Bk[x(n)]

· · ·

ik=ik−1

bk(i1, i2, . . . , ik)x(n − i1)x(n − i2) · · ·x(n − ik)

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� The input-output relation for each of these subsystems is asindicated below.

yk(n) = Bk[x(n)]

· · ·

ik=ik−1

bk(i1, i2, . . . , ik)x(n − i1)x(n − i2) · · ·x(n − ik)

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Volterra Filter - Figure

x(n) y(n)N [ ]

Figure 1: The input-output relationship of the nonlinear Volterra filter N [ · ].

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Volterra Filter - Sum of Subsystems

x(n) +

y (n)1

y (n)2

y (n)3

y (n)M

Figure 2: The nonlinear Volterra filter N as a sum of nonlinear subsystems Bk .

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Novel Representation

� In this section, the M th-order discrete, causal, time-invariantVolterra system is reformulated, and a new representation forthe Volterra system is given.

� This novel representation will enable us to devise an exactclosed form algorithm, for identifying the Volterra kernelsusing deterministic multilevel sequences, in the comingchapters.

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� The M th-order nonlinear system, N [ · ], under considerationcan be modelled by the triangular representation.

y(n) = N [x(n)] =M∑

yk(n) =M∑

Bk[x(n)]

· · ·

ik=ik−1

bk (i1, i2, . . . , ik) x(n − i1)x(n − i2) · · ·x(n − ik)

︸︷︷︸

Bk[x(n)]

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� The M th-order nonlinear system, N [ · ], under considerationcan be modelled by the triangular representation.

y(n) = N [x(n)] =M∑

yk(n) =M∑

Bk[x(n)]

· · ·

ik=ik−1

bk (i1, i2, . . . , ik) x(n − i1)x(n − i2) · · ·x(n − ik)

︸︷︷︸

Bk[x(n)]

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� We introduce a new representation for the Volterra system byrearranging the Volterra kernels.

� We propose that the output y(n) can be considered as the sumof the outputs of M different multivariate cross-term nonlinearsubsystems, H(`), ` = 1, . . . , M .

y(n) = N [x(n)] =M∑

y(`)(n) (8a)

y(`)(n) = H(`)[x(n)] (8b)

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y(n) = N [x(n)] =M∑

y(`)(n) (8a)

y(`)(n) = H(`)[x(n)] (8b)

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y(n) = N [x(n)] =M∑

y(`)(n) (8a)

y(`)(n) = H(`)[x(n)] (8b)

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y(n) = N [x(n)] =M∑

y(`)(n) (8a)

y(`)(n) = H(`)[x(n)] (8b)

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� The input-output relation for each of the subsystems H(`),` = 1, 2, . . . , M is as indicated below.

H(1)[x(n)] =N∑

(i) x(1)(n − i)

H(`)[x(n)] =

· · ·

Q`−1∑

q`−1=1

N−q`−1∑

(q1, . . . , q`−1; i) x(`)(q1, . . . , q`−1; n − i)

for 2 6 ` 6 M

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� The input-output relation for each of the subsystems H(`),` = 1, 2, . . . , M is as indicated below.

H(1)[x(n)] =N∑

(i) x(1)(n − i)

H(`)[x(n)] =

· · ·

Q`−1∑

q`−1=1

N−q`−1∑

(q1, . . . , q`−1; i) x(`)(q1, . . . , q`−1; n − i)

for 2 6 ` 6 M

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Novel Representation - Figure

x(n) + y(n)

y (n)(1)

y (n)(2)

y (n)(3)

y (n)(M)

Figure 3: The novel decomposition for the nonlinear Volterra filter N as a sum of cross-term subsys-tems H(`), ` = 1, . . . , M .

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Novel Representation - Figure

x(n) + y(n)

y (n)(1)

y (n)(2)

y (n)(3)

y (n)(M)

Figure 3: The novel decomposition for the nonlinear Volterra filter N as a sum of cross-term subsys-tems H(`), ` = 1, . . . , M .

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� The symbol H(`)[·] is called as an `-D cross-term Volterrasubsystem and h(`)(q1, . . . , q`−1; i) is called as an `-D kernelvector.

� The 1-D kernel vectors h(1)(i) and the corresponding inputvectors x(1)(n) can be given in terms of the triangular kernelsand the input signal x(n), respectively.

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� The symbol H(`)[·] is called as an `-D cross-term Volterrasubsystem and h(`)(q1, . . . , q`−1; i) is called as an `-D kernelvector.

� The 1-D kernel vectors h(1)(i) and the corresponding inputvectors x(1)(n) can be given in terms of the triangular kernelsand the input signal x(n), respectively.

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h(1)(i) =

h(1)1 (i)

h(1)2 (i)

(1)M (i)

b2(i, i)...

bM (i, . . . , i)

x(1)(n) =

x2(n)...

xM (n)

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� The `-D input vector in (9) can be expressed in the followingform:

x(`) (q1, . . . , q`−1; n) =

x(`)` (q1, . . . , q`−1; n)

x(`)`+1(q1, . . . , q`−1; n)

(`)M (q1, . . . , q`−1; n)

in which

x(`)` (q1, . . . , q`−1; n) = x(n) x(n − q1) · · · x(n − q1 − · · · − q`−1) (13)

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x(`) (q1, . . . , q`−1; n) =

x(`)` (q1, . . . , q`−1; n)

x(`)`+1(q1, . . . , q`−1; n)

(`)M (q1, . . . , q`−1; n)

in which

x(`)` (q1, . . . , q`−1; n) = x(n) x(n − q1) · · · x(n − q1 − · · · − q`−1) (13)

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x(`) (q1, . . . , q`−1; n) =

x(`)` (q1, . . . , q`−1; n)

x(`)`+1(q1, . . . , q`−1; n)

(`)M (q1, . . . , q`−1; n)

in which

x(`)` (q1, . . . , q`−1; n) = x(n) x(n − q1) · · · x(n − q1 − · · · − q`−1) (13)

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x(`) (q1, . . . , q`−1; n) =

x(`)` (q1, . . . , q`−1; n)

x(`)`+1(q1, . . . , q`−1; n)

(`)M (q1, . . . , q`−1; n)

in which

x(`)` (q1, . . . , q`−1; n) = x(n) x(n − q1) · · · x(n − q1 − · · · − q`−1) (13)

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x(`)k (q1, . . . , q`−1; n) ≡

x(p1,··· ,p`)k (q1, . . . , q`−1; n)

σ(p1,...,p`)(14)

� The subinput vector x(`)k (q1, . . . , q`−1; n) for k = `, ` + 1, . . . , M

in (12) consists of all possible input products of degree k.

x(p1,··· ,p`)k (q1, . . . , q`−1; n) = xp1(n)xp2(n−q1) · · · x

p`(n−q1−· · ·−q`−1)

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x(`)k (q1, . . . , q`−1; n) ≡

x(p1,··· ,p`)k (q1, . . . , q`−1; n)

σ(p1,...,p`)(14)

p`(n−q1−· · ·−q`−1)

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x(`)k (q1, . . . , q`−1; n) ≡

x(p1,··· ,p`)k (q1, . . . , q`−1; n)

σ(p1,...,p`)(14)

p`(n−q1−· · ·−q`−1)

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Example 2.1: We consider ` = 3 and M = 5. x(3) (q1, q2; n) will begiven as

x(3) (q1, q2; n) =

x(3)3 (q1, q2; n)

x(3)4 (q1, q2; n)

x(3)5 (q1, q2; n)

Here,x

(3)3 (q1, q2; n) = x(n) x(n − q1) x(n − q1 − q2) (17)

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For x(3)4 (q1, q2; n), the

)= 3 possible combinations can be

written as σ(p1, p2, p3) ={(2, 1, 1) (1, 2, 1), (1, 1, 2)

Hence, x(3)4 (q1, q2; n) is written as

x(3)4 (q1, q2; n) =

x2(n) x(n − q1) x(n − q1 − q2)

x(n) x2(n − q1) x(n − q1 − q2)

x(n) x(n − q1) x2(n − q1 − q2)

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For x(3)5 (q1, q2; n), all

)= 6 possible combinations can be written

σ(p1, p2, p3) ={(3, 1, 1) (2, 2, 1), (2, 1, 2), (1, 3, 1), (1, 2, 2), (1, 1, 3)

x(3)5 (q1, q2; n) =

x3(n) x(n − q1) x(n − q1 − q2)

x2(n) x2(n − q1) x(n − q1 − q2)

x2(n) x(n − q1) x2(n − q1 − q2)

x(n) x3(n − q1) x(n − q1 − q2)

x(n) x2(n − q1) x2(n − q1 − q2)

x(n) x(n − q1) x3(n − q1 − q2)

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For x(3)5 (q1, q2; n), all

σ(p1, p2, p3) ={(3, 1, 1) (2, 2, 1), (2, 1, 2), (1, 3, 1), (1, 2, 2), (1, 1, 3)

x(3)5 (q1, q2; n) =

x3(n) x(n − q1) x(n − q1 − q2)

x2(n) x2(n − q1) x(n − q1 − q2)

x2(n) x(n − q1) x2(n − q1 − q2)

x(n) x3(n − q1) x(n − q1 − q2)

x(n) x2(n − q1) x2(n − q1 − q2)

x(n) x(n − q1) x3(n − q1 − q2)

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For x(3)5 (q1, q2; n), all

σ(p1, p2, p3) ={(3, 1, 1) (2, 2, 1), (2, 1, 2), (1, 3, 1), (1, 2, 2), (1, 1, 3)

x(3)5 (q1, q2; n) =

x3(n) x(n − q1) x(n − q1 − q2)

x2(n) x2(n − q1) x(n − q1 − q2)

x2(n) x(n − q1) x2(n − q1 − q2)

x(n) x3(n − q1) x(n − q1 − q2)

x(n) x2(n − q1) x2(n − q1 − q2)

x(n) x(n − q1) x3(n − q1 − q2)

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� The `-D kernel vectors in (9) can be written in terms ofsubkernels as

h(`)(q1, . . . , q`−1; i) =

h(`)` (q1, . . . , q`−1; i)

h(`)`+1(q1, . . . , q`−1; i)

(`)M (q1, . . . , q`−1; i)

in which

h(`)` (q1, . . . , q`−1; i) = h

(1,1,··· ,1)` (q1, . . . , q`−1; i) (21)

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h(`)k (q1, . . . , q`−1; i) ≡

h(p1,p2,···p`)k (q1, . . . , q`−1; i)

σ(p1,p2,...,p`) (22)

� Here, the subkernel vector h(`)k (q1, . . . , q`−1; i) corresponds to

the subinput vector x(`)k (q1, . . . , q`−1; n − i) defined in (14).

h(`)k (q1, . . . , q`−1; i) consists of all the Volterra kernels of degree

k with ` cross-terms.

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Example 2.2: Continuing the previous example, let us consider` = 3 and M = 5.

h(3) (q1, q2; i) =

h(3)3 (q1, q2; i)

h(3)4 (q1, q2; i)

h(3)5 (q1, q2; i)

Here,h

(3)3 (q1, q2; i) = h

(1,1,1)3 (q1, q2; i) (24)

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h(3)4 (q1, q2; i) for ` = 3 can be written as,

h(3)4 (q1, q2; i) =

h(2,1,1)4 (q1, q2; i)

h(1,2,1)4 (q1, q2; i)

h(1,1,2)4 (q1, q2; i)

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h(3)5 (q1, q2; i) is written as,

h(3)5 (q1, q2; i) =

h(3,1,1)5 (q1, q2; i)

h(2,2,1)5 (q1, q2; i)

h(2,1,2)5 (q1, q2; i)

h(1,3,1)5 (q1, q2; i)

h(1,2,2)5 (q1, q2; i)

h(1,1,3)5 (q1, q2; i)

- p. 40

� There exists an equivalent triangular Volterra kernelbk(i1, i2, . . . , ik) as given in (7) for each component of thesubkernel vector h

(`)k (q1, . . . , q`−1; i),

� The relationship between the triangular Volterra kernels andcross-term Volterra kernels is as given below.

h(p1,p2,...,p`)k (q1, . . . , q`−1; i) =

bk(i, . . . , i︸︷︷︸

, i + q1, . . . , i + q1︸︷︷︸

, . . . , i + q`−1, . . . , i + q`−1︸︷︷︸

) (27)

- p. 40

(`)k (q1, . . . , q`−1; i),

h(p1,p2,...,p`)k (q1, . . . , q`−1; i) =

bk(i, . . . , i︸︷︷︸

, i + q1, . . . , i + q1︸︷︷︸

, . . . , i + q`−1, . . . , i + q`−1︸︷︷︸

) (27)

- p. 40

(`)k (q1, . . . , q`−1; i),

h(p1,p2,...,p`)k (q1, . . . , q`−1; i) =

bk(i, . . . , i︸︷︷︸

, i + q1, . . . , i + q1︸︷︷︸

, . . . , i + q`−1, . . . , i + q`−1︸︷︷︸

) (27)

- p. 41

Example 2.3: We continue with Example 2.2. We want to find therepresentations for the kernel vectors, but this time in terms ofthe Volterra kernels. The kernel vectors for ` = 3 and M = 5 cannow be written as

h(3)3 (q1, q2; i) = b3(i, i + q1, i + q2) (28)

h(3)4 (q1, q2; i) =

h(2,1,1)4 (q1, q2; i)

h(1,2,1)4 (q1, q2; i)

h(1,1,2)4 (q1, q2; i)

b4(i, i, i + q1, i + q2)

b4(i, i + q1, i + q1, i + q2)

b4(i, i + q1, i + q2, i + q2)

- p. 42

h(3)5 (q1, q2; i) =

h(3,1,1)5 (q1, q2; i)

h(2,2,1)5 (q1, q2; i)

h(2,1,2)5 (q1, q2; i)

h(1,3,1)5 (q1, q2; i)

h(1,2,2)5 (q1, q2; i)

h(1,1,3)5 (q1, q2; i)

b5(i, i, i, i + q1, i + q2)

b5(i, i, i + q1, i + q1, i + q2)

b5(i, i, i + q1, i + q2, i + q2)

b5(i, i + q1, i + q1, i + q1, i + q2)

b5(i, i + q1, i + q1, i + q2, i + q2)

b5(i, i + q1, i + q2, i + q2, i + q2)

- p. 43

Example 2.4: Let us give an example for the novel representationfor a system with M = 3 and N = 2. The usual (triangular)Volterra representation is given in the following form.

y(n) =2∑

b1(i1)x(n − i1) +2∑

b2(i1, i2)x(n − i1)x(n − i2)

b3(i1, i2, i3)x(n − i1)x(n − i2)x(n − i3)

- p. 44

y(n) = b1(0)x(n) + b1(1)x(n − 1) + b1(2)x(n − 2) + b2(0, 0)x2(n)

+ b2(1, 1)x2(n − 1) + b2(2, 2)x2(n − 2) + b2(0, 1)x(n)x(n − 1)

+ b2(1, 2)x(n − 1)x(n − 2) + b2(0, 2)x(n)x(n − 2) + b3(0, 0, 0)x3(n)

+ b3(1, 1, 1)x3(n − 1) + b3(2, 2, 2)x3(n − 2) + b3(0, 0, 1)x2(n)x(n − 1)

+ b3(1, 1, 2)x2(n − 1)x(n − 2) + b3(0, 0, 2)x2(n)x(n − 2)

+ b3(0, 1, 1)x(n)x2(n − 1) + b3(1, 2, 2)x(n − 1)x2(n − 2)

+ b3(0, 2, 2)x(n)x2(n − 2) + b3(0, 1, 2)x(n)x(n − 1)x(n − 2)

- p. 45

For this Volterra filter, using (8), the novel representation that weintroduced will be given as follows.

y(n) =2∑

(i) x(1)(n − i) +2∑

2−q1∑

(q1; i)x(2)(q1; n − i)

2−q1∑

2−q2∑

(q1, q2; i) x(3)(q1, q2; n − i)

(i) x(1)(n − i)

+ h(2)T

(1; 0)x(2)(1; n) + h(2)T

(1; 1)x(2)(1; n − 1) + h(2)T

(2; 0)x(2)(2; n)

+ h(3)T

(1, 1; 0) x(3)(1, 1; n)

- p. 46

h(1)(i) =

h(1)1 (i)

h(2)2 (i)

h(3)3 (i)

b2(i, i)

b3(i, i, i)

, for i = 0, 1, 2

x(1)(n − i) =

x(n − i)

x2(n − i)

x3(n − i)

, for i = 0, 1, 2

- p. 47

h(2)(1; 0) =

h(2)2 (1; 0)

h(2)3 (1; 0)

h(1,1)2 (1; 0)

h(2,1)3 (1; 0)

h(1,2)3 (1; 0)

b2(0, 1)

b3(0, 0, 1)

b3(0, 1, 1)

x(2)(1; n) =

x(2)2 (1; n)

x(2)3 (1; n)

x(2)2 (1; n)

x(2,1)3 (1; n)

x(1,2)3 (1; n)

x(n)x(n − 1)

x2(n)x(n − 1)

x(n)x2(n − 1)

- p. 48

h(2)(1; 1) =

h(2)2 (1; 1)

h(2)3 (1; 1)

h(1,1)2 (1; 1)

h(2,1)3 (1; 1)

h(1,2)3 (1; 1)

b2(1, 2)

b3(1, 1, 2)

b3(1, 2, 2)

x(2)(1; n − 1) =

x(2)2 (1; n − 1)

x(2)3 (1; n − 1)

x(2)2 (1; n − 1)

x(2,1)3 (1; n − 1)

x(1,2)3 (1; n − 1)

x(n − 1)x(n − 2)

x2(n − 1)x(n − 2)

x(n − 1)x2(n − 2)

- p. 49

h(2)(2; 0) =

h(2)2 (2; 0)

h(2)3 (2; 0)

h(1,1)2 (2; 0)

h(2,1)3 (2; 0)

h(1,2)3 (2; 0)

b2(0, 2)

b3(0, 0, 2)

b3(0, 2, 2)

x(2)(2; n) =

x(2)2 (2; n)

x(2)3 (2; n)

x(2)2 (2; n)

x(2,1)3 (2; n)

x(1,2)3 (2; n)

x(n)x(n − 2)

x2(n)x(n − 2)

x(n)x2(n − 2)

- p. 50

h(3)(1, 1; 0) =[

h(1,1,1)3 (1, 1; 0)

b3(0, 1, 2)]

x(3)(1, 1; n) =[

x(3)3 (1, 1; n)

x(n)x(n − 1)x(n − 2)]

- p. 51

� The novel cross-product kernel representation presented inthis section does not increase the number of kernels in theVolterra filter

� We have grouped the Volterra kernels in an novel mannerintroducing the concept of delay-wise dimensionality andcross-term subsystem, rather than using the multiplicationalorder of the Volterra kernels to group the kernels.

� This novel grouping enables us to devise an exact closed formalgorithm for identifying the Volterra kernels usingdeterministic multilevel sequences.

- p. 51

- p. 52

Novel Identification Algorithm

� Since Wiener introduced the use of the Volterra series fornonlinear modelling in engineering problems, researchershave developed several methods for the estimation of theVolterra kernels.

� The most common class of Volterra system identificationmethods include cross-correlation methods based on randominputs.

- p. 53

Novel Identification Algorithm

� We focus on deterministic excitation sequences for theidentification of nonlinear systems modelled using thetruncated Volterra series representation.

� We proposed a novel partitioning of the Volterra kernels. Thisrepresentation will result in simple closed form solutions forthe kernels when deterministic multilevel input sequences areused.

- p. 54

Novel Identification Algorithm - 1D

� In the novel representation we decomposed the output of theoverall nonlinear system in terms of the outputs of newlydefined cross-term subsystems H(`)[ · ], ` = 1, 2, . . . , M .

� We called the subsystem H(`)[ · ] as the `-D subsystem.� Multilevel single impulses, x(1)(m1; n) = am1δ(n), for

m1 = 1, 2, . . . ,(M1

)can be used to obtain the 1-D kernel vectors

in H(1)[ · ].� Using the novel cross-term representation,the higher

dimensional outputs are zero for these multilevel singleimpulses.

- p. 54

m1 = 1, 2, . . . ,(M1

- p. 54

� We called the subsystem H(`)[ · ] as the `-D subsystem.

� Multilevel single impulses, x(1)(m1; n) = am1δ(n), form1 = 1, 2, . . . ,

- p. 54

m1 = 1, 2, . . . ,(M1

in H(1)[ · ].

� Using the novel cross-term representation,the higherdimensional outputs are zero for these multilevel singleimpulses.

- p. 54

m1 = 1, 2, . . . ,(M1

- p. 55

N [am1δ(n)] = H(1)[am1δ(n)]

H(`)[am1δ(n)] = 0, for ` = 2, . . . , M(33)

This relation in (33) is shown pictorially in Fig. 4.

- p. 56

+ y(n)

x (m ;n)(1)

y (m ;n)(1)

Figure 4: Pictorial description for (33). The output of the nonlinear system N for x(n) = am1δ(n)

is equal to the output of the subsystem H(1).

- p. 57

� Hence, the output of the M th-order nonlinear system, whenthe input is x(n) = x(1)(m1; n), is given by

y(n) =N[

x(1)(m1; n)]

= y(m1; n)

=H(1)[

x(1)(m1; n)]

= y(1)(m1; n)

(i)u(1)(m1; n − i)

- p. 58

u(1)(m1; n)≡

x(1)(m1; n)

x(1)2(m1; n)...

(m1; n)

δ(n) (35)

- p. 59

� Here, m1 = 1, 2, . . . ,(M1

)denotes the ensemble index of the

input sequence. Now we can write all(M1

)outputs in the

ensemble matrix form as follows:

y(1)e (n) = N

x(1)e (n)

= H(1)[

x(1)e (n)

U(1)e (n − i) h(1)(i) (36)

where x(1)e (n),y

(1)e (n) and U

(1)e (n) denote the ensemble input,

ensemble output vectors and the input matrix, respectively.

- p. 60

x(1)e (n)≡

x(1)(1; n)

x(1)(2; n)...

x(1)(M ; n)

δ(n) (37)

y(1)e (n)≡

x(1)(1; n)]

x(1)(2; n)]

x(1)(M ; n)]

y(1)(1; n)

y(1)(2; n)...

y(1)(M ; n)

- p. 61

U(1)e (n)≡

(1; n)

(2; n)...

(M ; n)

- p. 62

� U(1)e (n − i) in (36) can be replaced with

U(1)e (n − i) = U

(1)e δ(n − i), and the matrix U

(1)e is written as

U(1)e =

a1 a21 · · · aM

a2 a22 · · · aM

.... . .

...aM a2

M · · · aMM

- p. 63

Novel Identification Algorithm -1D

� Hence, we get

y(1)e (n) =

U(1)e h(1)(i) δ(n − i) = U(1)

e h(1)(n) (41)

� Provided the inverse of the M × M matrix U(1)e exists, the 1-D

kernel vectors can be obtained as

h(1)(n) =[

y(1)e (n), for n = 0, 1, . . . , N (42)

� Fig. 5 depicts the identification method for 1-D kernels asoutlined in this section and finalized in (42).

- p. 63

� Hence, we get

y(1)e (n) =

U(1)e h(1)(i) δ(n − i) = U(1)

e h(1)(n) (41)

h(1)(n) =[

y(1)e (n), for n = 0, 1, . . . , N (42)

- p. 63

� Hence, we get

y(1)e (n) =

U(1)e h(1)(i) δ(n − i) = U(1)

e h(1)(n) (41)

h(1)(n) =[

y(1)e (n), for n = 0, 1, . . . , N (42)

- p. 64

(1)(n) y e(1)(n) h(1)(n)

(1) -1d(n)

Figure 5: Method used for identification of 1-D Volterra kernels, h(1)(n).

� Note that the linear FIR filter identification via the impulseresponse is covered by this method as the special case M = 1.

- p. 64

(1)(n) y e(1)(n) h(1)(n)

(1) -1d(n)

Figure 5: Method used for identification of 1-D Volterra kernels, h(1)(n).

� Note that the linear FIR filter identification via the impulseresponse is covered by this method as the special case M = 1.

- p. 65

� In order to guarantee the Vandermonde like input matrix U(1)e

in (40) to be nonsingular, the levels of the multilevel ensembleinputs must be chosen to be distinct and nonzero,

i.e., ai 6= 0

and ai 6= aj , ∀ i 6= j.

- p. 65

� In order to guarantee the Vandermonde like input matrix U(1)e

in (40) to be nonsingular, the levels of the multilevel ensembleinputs must be chosen to be distinct and nonzero, i.e., ai 6= 0

and ai 6= aj , ∀ i 6= j.

- p. 66

� Now we are interested in computing the 2-D kernel vectors,h(2)(q1; i) for q1 = 1, . . . , N and i = 0, 1, . . . , N − q1.

� We use 2-D ensemble inputs which consist of two impulseswith distinct amplitudes.

� The following sequence consisting of two impulses withdistinct amplitudes will only excite the 2-D kernel vectorh(2)(q1; n − q1) and the 1-D kernel vectors h(1)(n) andh(1)(n − q1).

- p. 66

- p. 67

x(2)((m1, m2), q1; n

)= x(1)(m1; n) + x(1)(m2; n − q1)

= am1δ(n) + am2δ(n − q1)

for m1 = 1, . . . , M − 1; m2 = m1 + 1, . . . , M

� It is possible to show that the 2-D input signal in (43) does notexcite the Volterra kernels having more than two cross-terms,i.e.,

x(2)((m1; m2), q1; n

= 0 for ` > 3. (44)

� (44) is depicted in Fig. 6.

- p. 67

x(2)((m1, m2), q1; n

)= x(1)(m1; n) + x(1)(m2; n − q1)

= am1δ(n) + am2δ(n − q1)

for m1 = 1, . . . , M − 1; m2 = m1 + 1, . . . , M

x(2)((m1; m2), q1; n

= 0 for ` > 3. (44)

- p. 67

x(2)((m1, m2), q1; n

)= x(1)(m1; n) + x(1)(m2; n − q1)

= am1δ(n) + am2δ(n − q1)

for m1 = 1, . . . , M − 1; m2 = m1 + 1, . . . , M

x(2)((m1; m2), q1; n

= 0 for ` > 3. (44)

- p. 68

y ((m ,m ),q ;n)(2)

1 2 1x ((m ,m ),q ;n)(2)

1 2 1 v ((m ,m ),q ;n)

Figure 6: Pictorial description for (44). The output of the nonlinear system N , for x(n) =

am1δ(n) + am2

δ(n − q1), is equal to the sum of the outputs of subsystems H(1) and H(2).

- p. 69

� Hence, the output for the input in (43) can be written as

x(2)((m1, m2),q1; n

= y(2)((m1, m2), q1; n

=H(1)[

x(2)((m1, m2), q1; n

+ H(2)[

x(2)((m1, m2), q1; n

� where

x(2)((m1, m2), q1; n

= v(2,2)((m1, m2), q1; n

- p. 69

� Hence, the output for the input in (43) can be written as

x(2)((m1, m2),q1; n

= y(2)((m1, m2), q1; n

=H(1)[

x(2)((m1, m2), q1; n

+ H(2)[

x(2)((m1, m2), q1; n

� where

x(2)((m1, m2), q1; n

= v(2,2)((m1, m2), q1; n

- p. 70

x(2)((m1, m2), q1; n

= v(2,1)((m1, m2), q1; n

= v(1,1)(m1; n) + v(1,1)(m2; n − q1)(47)

withv(1,1)(mi; n) = H(1)

x(1)(mi; n)]

- p. 71

v(i,j)((m1, . . . , mi), q1, . . . , qi−1; n

)= H(j)

x(i)((m1, . . . , mi), q1, . . . , qi−1; n

(48)The output of the 2-D subsystem can be obtained from (45), (46)and (47) as

v(2,2)((m1, m2), q1; n

x(2)((m1, m2), q1; n

v(1,1)(m1; n) + v(1,1)(m2; n − q1)]

- p. 72

� The output of the 2-D subsystem is obtained by subtractingthe appropriate previously computed 1-D outputs from theoverall nonlinear system output.

� We observe the outputs for(M2

)distinct 2-D ensemble inputs

which are given in the matrix form as follows:

x(2)e (q1; n) = T

(M)2,1 x(1)

e (n) + T(M)2,2 x(1)

e (n − q1) (50)

x(1)e (n) =

a1 a2 · · · aM

]Tδ(n)

- p. 72

� The output of the 2-D subsystem is obtained by subtractingthe appropriate previously computed 1-D outputs from theoverall nonlinear system output.

� We observe the outputs for(M2

)distinct 2-D ensemble inputs

which are given in the matrix form as follows:

x(2)e (q1; n) = T

(M)2,1 x(1)

e (n) + T(M)2,2 x(1)

e (n − q1) (50)

x(1)e (n) =

a1 a2 · · · aM

]Tδ(n)

- p. 73

� The constant T(M)2,1 and T

(M)2,2 matrices with

)rows and

columns are used to determine the necessary(M2

combinations of(M1

)= M ensemble inputs when taken two at

a time.

� These matrices are called as the ensemble input formationmatrices.

- p. 73

� The constant T(M)2,1 and T

(M)2,2 matrices with

)rows and

columns are used to determine the necessary(M2

combinations of(M1

)= M ensemble inputs when taken two at

a time.� These matrices are called as the ensemble input formation

matrices.

- p. 74

� Similar to the single-input single-output case in (45), using the2-D ensemble input vector in (50), we can determine thecorresponding output vectors of 1-D and 2-D subsystems,v

(2,1)e (q1; n) = H(1)

x(2)e (q1; n)

v(2,2)e (q1; n) = H(2)

x(2)e (q1; n)

, respectively.

� The notation v(i,j)e (q1, . . . , qi−1; n) will denote the output of the

j-D subsystem for an i-D input ensemble.

v(i,j)e (q1, . . . , qi−1; n) = H(j)

x(i)e (q1, . . . , qi−1; n)

- p. 74

(2,1)e (q1; n) = H(1)

x(2)e (q1; n)

v(2,2)e (q1; n) = H(2)

x(2)e (q1; n)

, respectively.

v(i,j)e (q1, . . . , qi−1; n) = H(j)

x(i)e (q1, . . . , qi−1; n)

- p. 74

(2,1)e (q1; n) = H(1)

x(2)e (q1; n)

v(2,2)e (q1; n) = H(2)

x(2)e (q1; n)

, respectively.

v(i,j)e (q1, . . . , qi−1; n) = H(j)

x(i)e (q1, . . . , qi−1; n)

- p. 75

The response of the 1-D system to the 2-D input ensemble can bedecomposed in terms of the 1-D responses as

v(2,1)e (q1; n) = H(1)

T(M)2,1 x(1)

e (n)]

+ H(1)[

T(M)2,2 x(1)

e (n − q1)]

= T(M)2,1 v(1,1)

e (n) + T(M)2,2 v(1,1)

e (n − q1)(52)

- p. 76

The response of the 2-D subsystem can be obtained bysubtracting the response of the 1-D subsystem from thenonlinear system output y

(2)e (q1; n) = N [x

(2)e (q1; n)]

v(2,2)e (q1; n) = y(2)

e (q1; n) − v(2,1)e (q1; n)

= y(2)e (q1; n) −

T(M)2,1 v(1,1)

e (n) + T(M)2,2 v(1,1)

e (n − q1)]

- p. 77

It is possible to write the 2-D subsystem equation for theensemble input case,

v(2,2)e (q1; n) =

N−q1∑

U(2)e (q1; n − i)h(2)(q1; i)

= U(2)e h(2)(q1; n − q1)

Similar to the 1-D case, U(2)e (q1; n − i) is replaced with

U(2)e δ(n − q1 − i). U

(2)e has the dimension

), and it is

written in terms of the amplitude levels, a1, a2, . . . , aM .

- p. 78

As an example, for M = 3 and M = 4, the U(2)e matrices will be

given respectively as follows.

U(2)e =

a1a2 a1a22 a2

a1a3 a1a23 a2

a2a3 a2a23 a2

U(2)e =

a1a2 a1a22 a2

1a2 a1a32 a2

1a22 a3

a1a3 a1a23 a2

1a3 a1a33 a2

1a23 a3

a1a4 a1a24 a3

1a4 a1a34 a2

1a24 a3

a2a3 a2a23 a2

2a3 a2a33 a2

2a23 a3

a2a4 a2a24 a2

2a4 a2a34 a2

2a24 a3

a3a4 a3a24 a2

3a4 a3a34 a2

3a24 a3

- p. 79

The 2-D Volterra kernel vectors are obtained as

h(2)(q1; n − q1) =[

v(2,2)e (q1; n) (55)

for q1 = 1, . . . , N and n = q1, q1 + 1, . . . , N, provided the inverseexists.Fig. 7 depicts the identification method for 2-D kernels asoutlined in this section and in (55).

- p. 80

x e(1)(n)

x e(2)(q ;n)

1 y e(2)(q ;n)

v e(1,1) (n)

ve(2,2) (q ;n)

1(2)h (q ;n-q )

1 1+T2,2

T2,1(M)

(2) -1

Ma x e(1)(n)

T2,1(M) T2,2

ve(2,1) (q ;n)

Figure 7: Method used for identification of 2-D Volterra kernels, h(2)(q1; n).

- p. 81

� The 3-D kernel vectors h(3)(q1, q2; i) are determined by usingthe 3-D ensemble responses along with the responses of the1-D and 2-D subsystems.

� The following 3-D ensemble input with three distinct impulsesexcites only 1-D, 2-D and 3-D subsystems.

x(3)e (q1, q2; n) = T

(M)3,1 x(1)

e (n)+T(M)3,2 x(1)

e (n−q2)+T(M)3,3 x(1)

e (n−q1−q2)

- p. 81

x(3)e (q1, q2; n) = T

(M)3,1 x(1)

e (n)+T(M)3,2 x(1)

e (n−q2)+T(M)3,3 x(1)

e (n−q1−q2)

- p. 81

x(3)e (q1, q2; n) = T

(M)3,1 x(1)

e (n)+T(M)3,2 x(1)

e (n−q2)+T(M)3,3 x(1)

e (n−q1−q2)

- p. 81

x(3)e (q1, q2; n) = T

(M)3,1 x(1)

e (n)+T(M)3,2 x(1)

e (n−q2)+T(M)3,3 x(1)

e (n−q1−q2)

- p. 82

The T(M)3,1 , T(M)

3,2 and T(M)3,3 input formation matrices with

rows and(M1

)columns are used to determine the necessary

combinations of the multilevel impulse functions, when takingtriplets at a time.

- p. 83

The output of the nonlinear system can be written as the sum ofthe outputs of the exited subsystems,

y(3)e (q1, q2; n) =N

x(3)e (q1, q2; n)

v(3,i)e (q1, q2; n)

The input-output relationship in (57) is depicted pictorially inFig. 8.

- p. 83

y(3)e (q1, q2; n) =N

x(3)e (q1, q2; n)

v(3,i)e (q1, q2; n)

- p. 83

y(3)e (q1, q2; n) =N

x(3)e (q1, q2; n)

v(3,i)e (q1, q2; n)

- p. 83

y(3)e (q1, q2; n) =N

x(3)e (q1, q2; n)

v(3,i)e (q1, q2; n)

- p. 83

y(3)e (q1, q2; n) =N

x(3)e (q1, q2; n)

v(3,i)e (q1, q2; n)

- p. 84

H (M) Z0

x e(3)(q ,q ;n)

v e(3,1) (q ,q ;n)

v e(3,2) (q ,q ;n)

v e(3,3) (q ,q ;n)

1 2 y e(3)(q ,q ;n)

Figure 8: Pictorial description for (57). The output of the nonlinear system N for the 3-D inputensemble x

(q1, q2; n), is equal to the sum of the outputs of subsystems H(1), H(2) and H(3).

- p. 85

� The output of the 1-D subsystem for the 3-D ensemble inputcan be written as a sum of the 1-D ensemble outputs.

v(3,1)e (q1, q2; n) =

(31)∑

S(M)31,j v

(1,1)e (n − n

(3,1)j ) (58)

� The matrices S(M)31,j for j = 1, 2, 3 are used to pick up the

appropriate 1-D ensemble output values.� We call these matrices as the output pick-up matrices.

- p. 85

v(3,1)e (q1, q2; n) =

(31)∑

S(M)31,j v

(1,1)e (n − n

(3,1)j ) (58)

- p. 85

v(3,1)e (q1, q2; n) =

(31)∑

S(M)31,j v

(1,1)e (n − n

(3,1)j ) (58)

appropriate 1-D ensemble output values.

� We call these matrices as the output pick-up matrices.

- p. 85

v(3,1)e (q1, q2; n) =

(31)∑

S(M)31,j v

(1,1)e (n − n

(3,1)j ) (58)

- p. 86

It is also possible to determine the responses of the 2-Dsubsystem for the 3-D ensemble inputs,

v(3,2)e (q1, q2; n) =

(32)∑

S(M)32,j v

(2,2)e (q

(3,2)j ; n − n

(3,2)j ) (59)

where the output pick up matrices S(M)32,j for j = 1, 2, 3, which

have(M3

)rows and

)columns, are used to determine the

appropriate 2-D ensemble output values for x(3)e (q1, q2; n).

- p. 87

The output of the 3-D subsystem v(3,3)e (q1, q2; n) can be written as

v(3,3)e (q1, q2; n) = U(3)

e h(3)(q1, q2; n − q1 − q2) (60)

The matrix U(3)e has the dimensions

As an example, for M = 4 and ` = 3, the matrix U(3)e will be given

U(3)e =

a1a2a3 a1a2a23 a1a

22a3 a2

a1a2a4 a1a2a24 a1a

22a4 a2

a1a3a4 a1a3a24 a1a

23a4 a2

a2a3a4 a2a3a24 a2a

23a4 a2

- p. 88

From (57)-(60), we get the desired calculation formula for the 3-Dkernel vectors.

h(3)(q1, q2; n − q1 − q2) =[

v(3,3)e (q1, q2; n) (61)

where,

v(3,3)e (q1, q2; n) = y(3)

e (q1, q2; n)

( 3∑

S(M)31,j v

(1,1)e (n − n

(3,1)j ) +

S(M)32,j v

(2,2)e (q

(3,2)j ; n − n

(3,2)j )

- p. 89

Fig. 9 depicts the identification method for 3-D kernels asdescribed by (61) and (62).

x e(1)(n)

x e(2)(q ;n)

1 y e(2)(q ;n)

v e(1,1) (n)

ve(2,2) (q ;n)

1+T2,2

T2,1(M)

ve(2,1) (q ;n)

z-q1-q

x e(3)(q ,q ;n)

1 2 y e(3)(q ,q ;n)

v e(3,2) (q ,q ;n)

1 2 v e(3,3) (q ,q ;n)

1 2h(3)

1 2 (q ,q ;n-q -q )

21+T3,1

T3,2(M)

T3,3(M)

(3) -1--

Ma x e(1)(n)d(n)

T2,1(M) T2,1

v e(3,1) (q ,q ;n)

S32,1(M)

z-n1(3,2)

S32,2(M)

z-n2(3,2)

S32,3(M)

z-n3(3,2)

S31,1(M)

z-n1(3,1)

S31,2(M)

z-n2(3,1)

S31,3(M)

z-n3(3,1)

Figure 9: Method used for identification of 3-D Volterra kernels, h(3)(q1, q2; n).

- p. 90

Novel Identification Algorithm - `−D

� Now, we try to identify the `-D kernel vectors by using theresponse of the nonlinear system for the `-D ensemble inputvector and all the previously computed subsystem outputs,v

(k,k)e (q1, . . . , qk−1; n).

� Similar to 1-, 2-, and 3-D ensemble input vectors, the `-D inputensemble vector can be written using the input formationmatrices and the 1-D input ensemble.

x(`)e (q1, . . . , q`−1; n) =

T(M)`,i x(1)

e (n − n(`)i ) (63)

- p. 90

(k,k)e (q1, . . . , qk−1; n).

x(`)e (q1, . . . , q`−1; n) =

T(M)`,i x(1)

e (n − n(`)i ) (63)

- p. 90

(k,k)e (q1, . . . , qk−1; n).

x(`)e (q1, . . . , q`−1; n) =

T(M)`,i x(1)

e (n − n(`)i ) (63)

- p. 90

(k,k)e (q1, . . . , qk−1; n).

x(`)e (q1, . . . , q`−1; n) =

T(M)`,i x(1)

e (n − n(`)i ) (63)

- p. 91

� The response of the nonlinear system to the ensemble input in(63) can be written in terms of the outputs of the subsystems,