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APPLICATIONS OF ANN IN MICROWAVE ENGINEERING
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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
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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
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Feedforward Neural Model
Output lines
Hidden layer
Input lines
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Topics Covered
Smart antennae modeling Demand node concept
1. Initialization & selection2. Adaptation3. Optimization
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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.
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ANN Model for Resonant Frequency
Rectangular Patch Antenna
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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
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Bandwidth of Patch Antenna
Rectangular Patch Antenna
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Algorithm’s used
• Back Propagation
• Delta – Bar – Delta (DBD)
• Extended DBD (EDBD)
• Quick Propagation
•ANN structure: 3481
•Max. no. of iterations: 5,00,000
•Tolerance (RMS Error): 0.015
Rectangular Patch Antenna
Other Details
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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
Network Parameters
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Demand Node Concept
Demand Node Concept
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Geographical map
Land use categories interference distance
Stochastic channel characteristics
Input Step Output
Mobile network
Morphology model
Estimated tx locationRadio network definition
Propagation analysis
Frequency allocation
Radio network analysis
Coverage
Freq plan
Network performance
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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 attractionOr increasing power.
Change position for repulsion orDecreasing power.
Y
N
YNo.of selection Values=preset
Val.?
N
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Adaptation
E1=Energy of current systemState z1
Determine transmitting Station tworst
Change position
Determine supplying areas
ChangePowerDisplace
T N
Y
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Optimization
E2=Energy of current System state z2
Choose randomNumber r
P:=prob(znew=zp)
Regenerate state Z1
Reduce temperature
P<r ?
E1—e2<0 ?
Steady state System ?
Y
NY
N
N
EndY
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D1 D2 D3 D4 D5 D6
Displacement:Case Of Attraction
Base station Sensory neuron
Area of coverage
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Borders of supplying areas.
Sensory neurons
Base station locations
BEFORE AFTER
Displacement:Case Of Repulsion
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Borders of the supplying areas.
Sensory neurons.Base station locations
BEFORE AFTER
Power Enhancement
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Sensory neuronsBorders of the supplying areas
Base station
Before After
Power Decrement
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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
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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.
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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.
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Thank You