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APPLICATIONS OF ANN IN MICROWAVE ENGINEERING www.fakengineer.com 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 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 www.fakengineer.com Feedforward Neural Model Output lines Hidden layer Input lines www.fakengineer.com Topics Covered Smart antennae modeling Demand node concept 1. 2. 3. Initialization & selection Adaptation Optimization 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. www.fakengineer.com ANN Model for Resonant Frequency Rectangular Patch Antenna www.fakengineer.com Training/Network Parameters Network size: Learning Rate: Momentum: Time Step for integration: Training Time: No. of Epochs: 5 40 1 0.08 0.205 5 10-10 6.4 min. 15000 www.fakengineer.com Bandwidth of Patch Antenna Rectangular Patch Antenna www.fakengineer.com Rectangular Patch Antenna Algorithm’s used • Back Propagation • Delta – Bar – Delta (DBD) • Extended DBD (EDBD) • Quick Propagation Other Details •ANN structure: 3481 •Max. no. of iterations: 5,00,000 •Tolerance (RMS Error): 0.015 www.fakengineer.com 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 www.fakengineer.com Demand Node Concept Demand Node Concept www.fakengineer.com Input Geographical map Morphology model Step Output Radio network definition Estimated tx location Propagation analysis Coverage Land use categories interference distance Frequency allocation Freq plan Stochastic channel characteristics Radio network analysis Network performance Mobile network www.fakengineer.com 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? Multiply supplied? No.of selection Y Y Values=preset Change position for Change position for Val.? Y attraction repulsion or Or increasing power. Decreasing power. www.fakengineer.com Adaptation E1=Energy of current system State z1 Determine transmitting Station tworst Displace T Y Change position Determine supplying areas www.fakengineer.com N Change Power Optimization E2=Energy of current System state z2 E1—e2<0 ? N Choose random Number r P:=prob(znew=zp) Y N P<r ? Y Regenerate state Z1 N Steady state System ? Reduce temperature Y End www.fakengineer.com Displacement:Case Of Attraction D1 D2 D3 D4 D5 D6 Base station Area of coverage www.fakengineer.com Sensory neuron Displacement:Case Of Repulsion Base station locations BEFORE Sensory neurons Borders of supplying areas. www.fakengineer.com AFTER Power Enhancement Sensory neurons. Base station locations BEFORE Borders of the supplying areas. www.fakengineer.com AFTER Power Decrement Borders of the supplying areas Sensory neurons Base station After Before www.fakengineer.com 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. www.fakengineer.com 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. 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. www.fakengineer.com Thank You www.fakengineer.com