Ann

Technical Seminar Presentation
Technology
APPLICATIONS OF ANN IN
MICROWAVE ENGINEERING
& Science Presented by
Amit Kumar Das
Roll# EC200147223
of Institute At
NIST,Berhampur
National Under the guidance of
Mr. Rowdra Ghatak
Amit Kumar Das Roll#EC200147223
[1]

Introduction
Technology
 ANNs are neuroscience -inspired computational tools.
 Learn from experience/examples (training) & not the example
itself.
& Science Generalize automatically as a results of their structure (not by
using human intelligence embedded in the form of ad hoc
computer programs).
of Institute  Used extensively for visual pattern recognition, speech
understanding, and more recently, for modeling and simulation of
complex processes.
National  Recently it has been applied to different branches of Microwave
Engineering
[2]

When To Apply ANN
Technology
 When the problem is poorly understood
& Science  When observations are difficult to carry out using noisy or
incomplete data
of Institute  When problem is complex, particularly while dealing with
nonlinear systems
National Amit Kumar Das Roll#EC200147223
[3]

Feedforward Neural Model
Technology
Output
lines
& Science Hidden
of layer
Institute National Input
lines
[4]

Topics Covered
Technology
 Smart antennae modeling
 Demand node concept
& Science 1. Initialization & selection
2. Adaptation
of Institute 3. Optimization
[5]

Smart Antenna Modeling
Technology
•A smart antenna consists of an antenna array combined with
& signal processing in both space and time.
Science •These systems of antennas include a large number of techniques
that attempt to enhance the received signal, suppress all
of interfering signals, and increase capacity, in general.
Institute National Amit Kumar Das Roll#EC200147223
[6]

ANN Model for Resonant Frequency
Rectangular Patch Antenna
Technology
& Science of Institute National Amit Kumar Das Roll#EC200147223
[7]

Training/Network Parameters
Technology
 Network size: 5 ´ 40 ´ 1
&  Learning Rate: 0.08
Science  Momentum: 0.205
 Time Step for integration: 5 ´ 10-10
of  Training Time: 6.4 min.
Institute  No. of Epochs: 15000
[8]

Bandwidth of Patch Antenna
Technology
& Science of Institute National Amit Kumar Das Roll#EC200147223
[9]

Technology
Algorithm’s used
Other Details
• Back Propagation
•ANN structure:
& Science • Delta – Bar – Delta
3´4´8´1
(DBD)
• Extended DBD (EDBD)
•Max. no. of iterations:
5,00,000
of • Quick Propagation
Institute •Tolerance (RMS Error):
0.015
[10]

Network Parameters
Technology
BP Parameters
EDBD Parameters
• Learning Coefficients:
• k= 0.095, k= 0.01,
a m – 0.3 for the 1st hidden
g= 0.0, g= 0.0
m a layer
f= 0.01, f= 0.1,
& • – m a 0.25 for the 2nd hidden
Science q = 0.7, l = 0.2,
layer
• – The sequential and/or random
0.15 for the output layer
training procedure follows
• momentum coefficient : 0.4
of QP Parameters
Institute DBD Parameters
• d = 0.0001
• k = 0.01, f = 0.5, q = 0.7, a
• a = 0.1
= 0.2
• • e = 1.0
Momentum coefficient = 0.4
• • m = 2.0
National The sequential and/or
random training procedure
follows
[11]

Demand Node Concept
Technology
& Science of Institute National Demand Node Concept
[12]

Input Step Output
Technology
Geographical map
Radio networEks tdimefainteidti toxn location
& Science Morphology model
Propagation analysis
Coverage
Land use categories
of interference
Frequency allocation
distance
Institute Freq plan
Stochastic channel
Radio network analysis
National characteristics
Network performance
Mobile network
[13]

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

Adaptation
Technology
E=Energy of current system
1State z1
& Science Determine transmitting
Station tworst
Change
Displace of T N
Power
Institute Y
Change position
National Determine supplying areas
[15]

Optimization
Technology
E=Energy of current
2System state z2
Ee<0 ?
Choose random
& 1—2N
Number r
Science Y
P:=prob(z=z)
newpof N
P<r ?
Institute Y
Regenerate state
ZN
1
Steady state
National Reduce temperature
System ?
Y End
[16]

Displacement:Case Of Attraction
Technology
& Science D1 D2 D3 D4 D5 D6
of Institute National Base station Sensory neuron
Area of coverage
[17]

Displacement:Case Of Repulsion
Technology
Base station
locations
Sensory
neurons
& Science of Institute Borders of
National BEFORE supplying areas.
AFTER
[18]

Power Enhancement
Technology
Sensory neurons.
Base station
& locations
Science of Institute BEFORE Borders of the
supplying areas.
AFTER
[19]

Power Decrement
Technology
Borders of the supplying areas
Sensory neurons
& Science of Institute Base station
National Before After
[20]

Emerging Trends / Future Applications
Technology
 To find the optimized compact structures for low-profile
antennas
 Applications in reconfigurable antennas/arrays
&  Science Applications in fractal antennas
 To increase the efficiency of numerical algorithms used in
antenna analysis like MoM, FDTD, FEM etc.
of Institute National Amit Kumar Das Roll#EC200147223
[21]

Conclusion
Technology
Neural networks mimics brain’s problem solving process
& this has been the motivating factor for the use of ANN
& where
Science  huge amount of data is involved.
 the sources vary.
of Institute  decision making is critical.
 environment is complex.
[22]

REFERENCES
[1]Haykin, S., 1999.Neural Networks A Comprehensive
Technology
Foundation, 2nd edition, Pearson Education.
[2]Freeman James A. & Skapura David M., Neural Networks,
Pearson Education.
& Science [3]Yuhas, Ben & Ansari Nerman. Neural Networks in
Telecommunications.
[4]B.Yegnanarayana. 1999.Artificial Neural Networks. Prentice
of Hall of India.
Institute [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.
National [6]N.K. Bose and P.Liang, Neural Network Fundamentals with
Graphs, Algorithms and Applications,McGraw-Hill,Int.
[23]

Technology
& Science Thank You
of Institute National Amit Kumar Das Roll#EC200147223
[24]

Ann

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