[1] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC APPLICATIONS OF ANN IN MICROWAVE ENGINEERING Presented by Amit Kumar Das Roll# EC At NIST,Berhampur Under the guidance of Mr. Rowdra Ghatak

[2] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Feedforward Neural Model Output lines Hidden layer Input lines

[5] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Topics Covered Smart antennae modeling Demand node concept 1. Initialization & selection 2. Adaptation 3. Optimization

[6] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC ANN Model for Resonant Frequency Rectangular Patch Antenna

[8] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Network size: 5  40  1 Learning Rate: 0.08 Momentum: Time Step for integration: 5  Training Time: 6.4 min. No. of Epochs: Training/Network Parameters

[9] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Bandwidth of Patch Antenna Rectangular Patch Antenna

[10] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Algorithm’s 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): Rectangular Patch Antenna Other Details

[11] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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

[12] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Demand Node Concept

[13] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC D1 D2 D3 D4 D5D6 Displacement:Case Of Attraction Base station Sensory neuron Area of coverage

[18] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Borders of supplying areas. Sensory neurons Base station locations BEFOREAFTER Displacement:Case Of Repulsion

[19] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Borders of the supplying areas. Sensory neurons. Base station locations BEFOREAFTER Power Enhancement

[20] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Sensory neurons Borders of the supplying areas Base station Before After Power Decrement

[21] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC 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.

[24] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC Thank You

[25] National Institute of Science & Technology Technical Seminar Presentation Amit Kumar Das Roll#EC