Neuro-Fuzzy Systems: Radical

International Journal of Advanced Engineering Research and Technology (IJAERT), ISSN: 2348–8190 ICRTIET-2014 Conference Proceeding, 30

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Divya Jyoti College of Engineering & Technology, Modinagar, Ghaziabad (U.P.), India

Neuro-Fuzzy Systems: Radical

Neha Kashyap

PG student, Department of Electrical Engineering

National Institute of Technical Teachers’ Training and

Research

Chandigarh, India

Nidhi Agarwal

PG student, Department of Electrical Engineering

National Institute of Technical Teachers’ Training and

Research

Chandigarh, India

ABSTRACT With the advancement of technologies the attention of

scientist is getting directed towards two highly

developed technical field – Artificial Neural Networks

and Fuzzy Logic Systems. But the combination of two

technologies is serving efficiently for many problems by

overcoming the limitation of each other. Such systems

are called Neuro-Fuzzy System. This paper provides a

detail review of Neuro-fuzzy system.

Index Terms— Neuro-Fuzzy Systems, Neuro-Fuzzy

Model, Neuro- Fuzzy architecture

INTRODUCTION Neuro –Fuzzy has been derived from two most

advanced recently developed technologies. One is

―Artificial Neural Network Technology‖ and other is

―Fuzzy logic systems‖. Neuro- Fuzzy is basically a

combination both the technologies. The word Neuro has

been extracted from Artificial NEURal and the word

Fuzzy has been extracted from FUZZY logic systems.

The limitations of the one technology has been

overcome by capabilities of the other technology, but the

common ground between the two is that, both of them are

inspired from human biological abilities.

Artificial neural is inspired from the capabilities of

human brain to learn, to generalize and to perform the

abstraction which is possible due to very highly complex

non-linear parallel computation structure of basic unit

known as neuron. Fuzzy logic system has been

influenced by the ability of human being to tact with

error and impression. Therefore, Fuzzy logic system

deals with impression of input and output of the system

by directly implementing fuzzy sets which provide wide

range to appropriately define system with great

flexibility and suppleness. Both the technologies provide

optimum and efficient solution for wide ranges of

problem with their merits and demerits. Each of the

technologies has its own advantages and disadvantages.

And the advantages of both the technologies have been

nobly utilized by Neuro-Fuzzy system.

Advantages of Artificial Neural Network are

discussed as follows:

Non-linearity that provides justification and

realization for quantitative conceptualization of

the problem under process.

Various types of Learning or Training

Mechanism just require modification of synaptic

weight link between Input neuron and Output

neuron.

Neural Networks have knack to adapt changes in

surrounding (external and internal) by changing

synaptic weight which make them appropriately

suitable for time variant applications.

High Robustness provides high degree of fault

tolerance. While designing the learning

algorithm various corrective measures are

employed which deals with the disturbances and

fault.

Provide output with high degree of confidence

with accurate decision.

Activities of one neuron affect globally other

neurons which provide contextual information

processing.

Identical universally for a system which help in

uniform analysis to develop algorithms and

theories.

While some disadvantages associated with Artificial

Neural are as follows:

Don’t deal with zero error and impression; deal

only with minimization of error to converge

system towards stability.

Artificial Neural Network requires training

which takes time and data, and data required for

training is limited to defined process and

problem, therefore can’t be utilized on wide

scope.

Out of sight nature of artificial neural network

makes it hard to analysis and to determine the

numbers of neurons/units and the numbers of

layers.

Other than neurons various other computational

resources are required to implement Artificial

Neural completely.

One good solution for a problem might not be

good solution for other problem.


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On other hand various shortcoming of artificial neural

network can be overcome by Fuzzy Logic Systems.

Various merits associated with FLC are as:

Deals with imprecision which make it capable to

represent uncertainties of knowledge.

Describe Input and Output with the help of

linguistic variables which make interpretation

about the system and interaction with system

easy.

Robustness is high since it addresses the

uncertainties, imprecision and disturbances.

Rule Base or Fuzzy set can be easily modified as

per requirements.

Rule Base are designed and built on expert

advice.

Non-linear function can be easily modeled.

While some demerits associated with Fuzzy logic

system:

Fuzzy output produced by rule base doesn’t give

accurate crisp decision.

Depends on the expert advice to design Rule

Base Logic.

Require time in modification, tuning and

simulation before completely implemented.

NEURO-FUZZY MODEL Both different technologies alone serve very well for

the various problems in the field of engineering, medical,

finance, credit evolution and other various area of

development and requirements, but if the benefits of both

the technologies are clubbed together to develop a

system which can control complex dynamic non-linear

system by two methods or models:

1) With fuzzy interface and Neural Processor to have

desired output:

To Linguistic variable, fuzzy interface interact and

provide input to Neural Network. Neural network is used

to provide desired output with evidential response. This

Model is illustrated in Figure II.1.

Figure II.1.

1) With Neural Driving System and Fuzzy

Processor to have desired output:

Neural Driving system responds to the input and

develops membership function to which fuzzy interface

respond according to the rule base and give the desired

output. This Model can be understood with the help of

figure II.2.

Figure II.1.

Defining lot of linguistic variable and developing

rule base which are encoded by expert take lot of time

but Neural Network due to its unique property of

adaptively make this process automated which reduces

the time and error and increases the efficiency and

accuracy of the system. These model mentioned above

help in developing the architecture for Neuro-fuzzy

systems.

NEURO-FUZZY SYSTEM ARCHITECTURE How Neuro-fuzzy can be hybrid? It doesn’t have

any defined standard as such but it is not even very

secondary or minor to built hybrid structure. Some major

work has been done regarding the developments of

architecture are as:

2) GARIC Architecture:

Generalized Approximate Reasoning based Intelligence

Control

GARIC [4] uses a neuro-fuzzy system which

contains two artificial neural networks system, one as

ASN (Action Selection Network) and other is as AEN

(Action State Evaluation Network) explained by figure

III.1. The ASN uses five layered network and the AEN

as the name suggest is an adaptative evaluator which

evaluates the process of ASN. The very first hidden layer

of ASN supplies the linguistics values of all associated

input variables. The next layer, the second hidden layer

provides the fuzzy rule base nodes to facilitate the

compatibility degree of each and every rule by using a

softmin operator. The third hidden layer represents the

output using linguistics variables. The synaptic weight

links connected between the layers are not weighted. The

result of all rule bases are calculated by the strength of

the rules antecedents calculated in the second layer

nodes of rule nodes. To calculate the output in third layer

of GARIC the mean of local mean of maximum scheme

is used. For fine result GARIC utilizes combination of

two famous well known neural learning mechanism one


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is Gradient descending and reinforcement learning

methods.

Figure III. 1. Action Selection Network of GARIC

3) FALCON architecture.

Fuzzy Adaptive learning Control Network-

FALCON [5] has five layered structure design

described in figure III.2. The first hidden layer represents

process of fuzzification of all input variables. The second

hidden layer represents the precondition of the rule base

that are followed by rule consequents in the next hidden

layer or third hidden layer. It has two layers for linguistic

variables for each output variables. One is used to train

the data (for the desired result) and the other one is used

for the measured output. FALCON utilizes a hybrid-

learning algorithm which is a combination of

unsupervised learning to trace initial membership

variables/ rule base and a gradient descent learning to

optimally regulate fuzzy input to produce the desired

outputs.

Figure III. 2. Architecture of FALCON

4) ANFIS architecture:

Adaptive Neuro Fuzzy Inference System-

ANFIS [6] have a five layered architecture

illustrated in Figure III.3. The first hidden layer

represents the process of fuzzification of all the input

variables. The second hidden layer employs Takagi

Sugeno FIS (also called T-norm operators) to calculate

the rule antecedent part. The third hidden layer regulates

the strength of the rule which is followed by the fourth

hidden layer in which the consequent variables of the

rule are determined. The output layer calculates the

whole input as the addition of all incoming signals.

ANFIS utilizes back propagation learning algorithm to

construct premise parameters (that learns the variables

associated to membership functions) and the error

correction/ least mean square estimation learning

algorithm to find out the consequent parameters. The

learning process consists of two steps: In the first step

the input to the model propagates through and the

optimal consequent are predictable by an iterative least

mean square learning algorithm, while the premise

variables are assumed to be constant for the cycle in

process for the period of the training set. In the second

step the model are propagated again, and in this span,

back propagation learning algorithm is used to adjust the

premise parameters, while the consequent parameters

remain unchanged. This process is then iterated.

Figure III.3. Architecture of ANFIS

5) NEFCON architecture:

NEuro-Fuzzy CONtrol-

In NEFCON [7] the input layer represents the

process of fuzzification. The output layer represents the

process of defuzzification. It utilizes Mamdani type FIS

and synaptic links, connection between the layers are

weighted by fuzzy sets. The learning procedure is

combination of two learning mechanism one is Back

Propagation Learning algorithm and other is

reinforcement learning. NEFCON can be explained with

the help of figure.III.4.


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Figure. III.4. Architecture of NEFCON

NEFCON currently have two versions:

A. NEFPROX (applied for the function

approximation)

B. NEFCLASS (applied for the classification

tasks).

6) FUN architecture:

FUzzy Net -

FUN [8] is muti-layed architecture.

Figure III.5. An example illustrating the architecture of

FUN

The first hidden layer represents the process of

fuzzification on input to the model and has the

membership functions. The second hidden layer, the

union (fuzzy-AND) are computed. The third layer stores

the membership functions of the output. Here, the

activation function deployed is a fuzzy-OR. The output

hidden layer represents the process of the

defuzzification. The model is initially started by fuzzy

rule base and the related membership functions. It uses

stochastic learning method that randomly modifies

variables of membership functions and synaptic links

within the network design; after random modification

cost function is applied that evaluates. If adjustment

improves then the modification is kept, or else it is not

done. Figure III.5. with the help of simple example

explains the architecture of FUN. Example explains how

to maintain the height of water level at the set point in

the system.

7) SONFIS architecture:

Self cOnstructing Neural Fuzzy Inference Network

SONFIN [9] utilizes Takagi-Sugeno Fuzzy

interface system. The input space is divided by the help

of clustering based algorithm and the identification of

the structure of the consequent part, a selected value

assigned to each rule initially by the help of clustering

process. For the process of identification of the

parameters, the consequent parameters are tuned

optimally by recursive least squares algorithms or by

least mean squares or the input space or parameters are

adjusted by back propagation algorithm. A Six layered

architecture of SONFIS has been shown in figure. III.6.

Figure. III.6. Architecture of NEFCON

8) EFuNN architecture

Evolving Fuzzy Neural Network-

In EFuNN [10] all nodes of all layers are created

during learning. The input hidden layer passes input to

the second hidden layer, which determines the fuzzy

membership degree to which the input variables are

associated to predetermine fuzzy membership functions.

The third layer represents the fuzzy rule base node which

defines the prototypes of input-output relation as the

association of the hyper-spheres from the fuzzy input

spaces to the fuzzy output spaces. Each rule base node is

described by two vectors of synaptic connection weights,

which are modified through the hybrid learning

mechanism. The fourth layer computes the degrees of

output membership variables which are matched by the

input, and the fifth layer represents the process of

defuzzification and determines the exact values of the

output.


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Figure. III.7. Architecture of EFuNN

9) dmEFuNN architecture:

Dynamic Evolving Fuzzy Neural-

dmEFuNN [11] is modified version of EFuNN. In

dmEFuNN the whole group of rule base nodes is

dynamically chosen for each and every new input and

the activation functions are used to compute the

dynamical variables of the output function. It employs

Takagi-Sugeno FIS which is based on a least squares/

Error correction learning algorithm.

10) EvoNF architecture:

Evolutionary Design of Neuro-Fuzzy Systems

EvoNF architecture [12] is the most recent developed

technique. In this, the architecture, the node, and the

learning mechanism are modified a with respect to a

five-tier hierarchical evolutionary scheme explained in

figure III.9.A.

Figure III.9.A. Herarchical Evolutionary Scheme

Figure III.9.B. Architecture of EvoNF

Any type of fuzzy interface system can be used such as

Mamdani FIS or Takagi Sugeno/ called T-norm

operators FIS. It will have first layer as input layer which

will perform the process of fuzzification and the second

layer defines the rule base which provide input to the

third layer for the Fuzzy output and then final output.

This architecture is described in figure III.9.B

CONCLUSION This paper present hybrid Neuro-Fuzzy system which

can be modeled by various methods using various

learning algorithm to develop training for neural network

and can be interfaced by using Takagi-Sugeno FIS or

Mamdani type FIS. Various aspects shows Neuro-Fuzzy

serves better result than utilizing Artificial Neural

Network or Fuzzy logic system technique individually.

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Neuro-Fuzzy Systems: Radical

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