1. www.fakengineer.com APPLICATIONS OF ANN IN MICROWAVE ENGINEERING

2. www.fakengineer.com 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

3. www.fakengineer.com 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

4. www.fakengineer.com Feedforward Neural Model Output lines Hidden layer Input lines

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

6. www.fakengineer.com 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.

7. www.fakengineer.com ANN Model for Resonant Frequency Rectangular Patch Antenna

8. www.fakengineer.com 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

9. www.fakengineer.com Bandwidth of Patch Antenna Rectangular Patch Antenna

10. www.fakengineer.com Algorithms used Back Propagation Delta – Bar – Delta (DBD) Extended DBD (EDBD) Quick Propagation ANN structure: 3 4 8 1 Max. no. of iterations: 5,00,000 Tolerance (RMS Error): 0.015 Rectangular Patch Antenna Other Details

11. www.fakengineer.com 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 = 0.0001 a = 0.1 = 1.0 m = 2.0 Network Parameters

12. www.fakengineer.com Demand Node Concept

13. www.fakengineer.com 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

14. www.fakengineer.com 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

15. www.fakengineer.com 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

16. www.fakengineer.com 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

17. www.fakengineer.com D1 D2 D3 D4 D5D6 Displacement:Case Of Attraction Base station Sensory neuron Area of coverage

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

19. www.fakengineer.com Borders of the supplying areas. Sensory neurons. Base station locations BEFOREAFTER Power Enhancement

20. www.fakengineer.com Sensory neurons Borders of the supplying areas Base station Before After Power Decrement

21. www.fakengineer.com 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

22. www.fakengineer.com 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.

23. www.fakengineer.com 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.

24. www.fakengineer.com Thank You