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APPLICATIONS OF ANN IN MICROWAVE ENGINEERING

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ANNs are neuroscience -inspired computational tools.

Learn from experience/examples (training) & not the example itself.

Generalize automatically as a results of their structure (not by using human intelligence embedded in the form of ad hoc computer programs).

Used extensively for visual pattern recognition, speech understanding, and more recently, for modeling and simulation of complex processes.

Recently it has been applied to different branches of Microwave Engineering

Introduction

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When To Apply ANN

When the problem is poorly understood

When observations are difficult to carry out using noisy or incomplete data

When problem is complex, particularly while dealing with nonlinear systems

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Feedforward Neural Model

Output lines

Hidden layer

Input lines

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Topics Covered

Smart antennae modeling Demand node concept

1. Initialization & selection2. Adaptation3. Optimization

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Smart Antenna Modeling

•A smart antenna consists of an antenna array combined with signal processing in both space and time.

•These systems of antennas include a large number of techniques that attempt to enhance the received signal, suppress all

interfering signals, and increase capacity, in general.

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ANN Model for Resonant Frequency

Rectangular Patch Antenna

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Network size: 5 40 1 Learning Rate: 0.08 Momentum: 0.205 Time Step for integration: 5 10-10

Training Time: 6.4 min. No. of Epochs: 15000

Training/Network Parameters

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Bandwidth of Patch Antenna

Rectangular Patch Antenna

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Algorithm’s used

• Back Propagation

• Delta – Bar – Delta (DBD)

• Extended DBD (EDBD)

• Quick Propagation

•ANN structure: 3481

•Max. no. of iterations: 5,00,000

•Tolerance (RMS Error): 0.015

Rectangular Patch Antenna

Other Details

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BP Parameters

• Learning Coefficients: – 0.3 for the 1st hidden

layer – 0.25 for the 2nd hidden

layer– 0.15 for the output layer

• momentum coefficient : 0.4

DBD Parameters• k = 0.01, = 0.5, = 0.7, a

= 0.2• Momentum coefficient = 0.4 • The sequential and/or

random training procedure follows

EDBD Parameters • k = 0.095, k = 0.01, gm = 0.0, g = 0.0 • m = 0.01, = 0.1, = 0.7, l = 0.2, • The sequential and/or random training procedure follows

QP Parameters• = 0.0001• a = 0.1 • = 1.0 • m = 2.0

Network Parameters

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Demand Node Concept

Demand Node Concept

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Geographical map

Land use categories interference distance

Stochastic channel characteristics

Input Step Output

Mobile network

Morphology model

Estimated tx locationRadio network definition

Propagation analysis

Frequency allocation

Radio network analysis

Coverage

Freq plan

Network performance

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Initialization & Selection

N

Y

Start

•Distribute sensory neurons.•Place transmitting stations

•Determine initial temperature.

Determine supplying areas.

Random selection of a Sensory neuron

No supply? Multiply supplied?

Change position for attractionOr increasing power.

Change position for repulsion orDecreasing power.

Y

N

YNo.of selection Values=preset

Val.?

N

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Adaptation

E1=Energy of current systemState z1

Determine transmitting Station tworst

Change position

Determine supplying areas

ChangePowerDisplace

T N

Y

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Optimization

E2=Energy of current System state z2

Choose randomNumber r

P:=prob(znew=zp)

Regenerate state Z1

Reduce temperature

P<r ?

E1—e2<0 ?

Steady state System ?

Y

NY

N

N

EndY

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D1 D2 D3 D4 D5 D6

Displacement:Case Of Attraction

Base station Sensory neuron

Area of coverage

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Borders of supplying areas.

Sensory neurons

Base station locations

BEFORE AFTER

Displacement:Case Of Repulsion

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Borders of the supplying areas.

Sensory neurons.Base station locations

BEFORE AFTER

Power Enhancement

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Sensory neuronsBorders of the supplying areas

Base station

Before After

Power Decrement

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To find the optimized compact structures for low-profile antennas

Applications in reconfigurable antennas/arrays Applications in fractal antennas To increase the efficiency of numerical algorithms used in

antenna analysis like MoM, FDTD, FEM etc.

Emerging Trends / Future Applications

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Conclusion

Neural networks mimics brain’s problem solving process & this has been the motivating factor for the use of ANN where

huge amount of data is involved.

the sources vary.

decision making is critical.

environment is complex.

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REFERENCES

[1]Haykin, S., 1999.Neural Networks A Comprehensive Foundation, 2nd edition, Pearson Education.

[2]Freeman James A. & Skapura David M., Neural Networks, Pearson Education.

[3]Yuhas, Ben & Ansari Nerman. Neural Networks in Telecommunications.

[4]B.Yegnanarayana. 1999.Artificial Neural Networks. Prentice Hall of India.

[5]G.A. Carpenter and S.Grossberg, ‘The ART of adaptive pattern recognition by a self-organization neural network’, IEEE Computer, vol. 21, pp. 77-88, 1988.

[6]N.K. Bose and P.Liang, Neural Network Fundamentals with Graphs, Algorithms and Applications,McGraw-Hill,Int.

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