APPLICATIONS OF ANN IN MICROWAVE ENGINEERING Presented by Amit Kumar Das

1. APPLICATIONS OF ANN IN MICROWAVE ENGINEERING Presented by Amit Kumar Das Roll# EC200147223 At NIST,Berhampur Under the guidance of Mr. Rowdra Ghatak

2. Introduction • 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

3. 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 When To Apply ANN

4. Feedforward Neural Model Output lines Hidden layer Input lines

5. Smart antennae modeling Demand node concept Initialization & selection Adaptation Optimization Topics Covered

6. 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. ANN Model for Resonant Frequency Rectangular Patch Antenna

8. Training/Network Parameters • 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

9. Bandwidth of Patch Antenna Rectangular Patch Antenna

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

11. Network Parameters • 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

12. Demand Node Concept Demand Node Concept

13. Input Step Output Radio network definition Geographical map Estimated tx location Propagation analysis Morphology model Coverage Land use categories interference distance Frequency allocation Freq plan Radio network analysis Stochastic channel characteristics Network performance Mobile network

14. Initialization & Selection Start • Distribute sensory neurons. • Place transmitting stations • Determine initial temperature. Determine supplying areas. Random selection of a Sensory neuron N N N No supply? No.of selection Values=preset Val.? Multiply supplied? Y Y Change position for attraction Or increasing power. Change position for repulsion or Decreasing power. Y

15. Adaptation E1=Energy of current system State z1 Determine transmitting Station tworst Change Power Displace T N Y Change position Determine supplying areas

16. Optimization E2=Energy of current System state z2 E1—e2<0 ? N Choose random Number r P:=prob(znew=zp) Y P<r ? N Y Regenerate state Z1 N Steady state System ? Reduce temperature Y End

17. Displacement:Case Of Attraction D1 D3 D5 D6 D2 D4 Sensory neuron Base station Area of coverage

18. Displacement:Case Of Repulsion Base station locations Sensoryneurons Borders of supplying areas. BEFORE AFTER

19. Power Enhancement Sensory neurons. Base station locations Borders of the supplying areas. BEFORE AFTER

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

21. Emerging Trends / Future Applications • 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.

22. 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.

23. 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. Thank You