APPLICATIONS OF ANN IN MICROWAVE ENGINEERING

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

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

Feedforward Neural Model Output lines Hidden layer Input lines

Topics Covered Smart antennae modeling Demand node concept 1. Initialization & selection 2. Adaptation 3. Optimization

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.

ANN Model for Resonant Frequency Rectangular Patch Antenna

Network size: Learning Rate: 0.08 Momentum: Time Step for integration: Training Time: 6.4 min. No. of Epochs: Training/Network Parameters

Bandwidth of Patch Antenna Rectangular Patch Antenna

Algorithms used Back Propagation Delta – Bar – Delta (DBD) Extended DBD (EDBD) Quick Propagation ANN structure: Max. no. of iterations: 5,00,000 Tolerance (RMS Error): Rectangular Patch Antenna Other Details

BP Parameters Learning Coefficients: –0.3 for the 1 st hidden layer –0.25 for the 2 nd 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, g m = 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 = a = 0.1 = 1.0 m = 2.0 Network Parameters

Demand Node Concept

Geographical map Land use categories interference distance Stochastic channel characteristics InputStepOutput Mobile network Morphology model Estimated t x location Radio network definition Propagation analysis Frequency allocation Radio network analysis Coverage Freq plan Network performance

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 attraction Or increasing power. Change position for repulsion or Decreasing power. Y N Y No.of selection Values=preset Val.? N

Adaptation E 1 =Energy of current system State z 1 Determine transmitting Station t worst Change position Determine supplying areas Change Power Displace T N Y

Optimization E 2 =Energy of current System state z 2 Choose random Number r P:=prob(z new =z p ) Regenerate state Z 1 Reduce temperature P<r ? E 1 e 2 <0 ? Steady state System ? Y N Y N N End Y

D1 D2 D3 D4 D5D6 Displacement:Case Of Attraction Base station Sensory neuron Area of coverage

Borders of supplying areas. Sensory neurons Base station locations BEFOREAFTER Displacement:Case Of Repulsion

Borders of the supplying areas. Sensory neurons. Base station locations BEFOREAFTER Power Enhancement

Sensory neurons Borders of the supplying areas Base station Before After Power Decrement

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

Conclusion Neural networks mimics brains 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.

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

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