NEURAL NETWORKS AND FUZZY SYSTEM Module 1:

glassesbeepingAI and Robotics

Oct 20, 2013 (3 years and 11 months ago)

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NEURAL NETWORKS AND FUZZY SYSTEM

Module 1:

Fundamental concepts
, Introduction to artificial neural network
s
(ANN), supervised & unsupervised
learning, error correction learn
ing, H
ebbian learning, competitive learning, Kohonen self
-
organizing
networks

Module 2:

Single neuron/ perceptron networks: training methodology, application to
linearly
separable problems,
multilayer perceptron

networks
, back propagation algorithm, virtues and limitations of back
propagation, methods of speeding

Module 3:


Radial b
asis function networks, interpolation problems, covers

theorem, regularization networks,
fun
c
tional expansion networks
:

functional link artificial neural network

(FLANN), Cascaded FLANN

Module 4:

Fuzzy set theory & rules:

Introduction to fuzzy systems, mem
bership funcation, fuzzy union, intersection and complement, fuzzy
relational operations, fuzzy IF THEN rules, fuzzy reasoning

Module 5:

Fuzzy inference systems:

Introduction, Mamdani fu
zzy model, Sugeno fuzzy model, T
ekamoto fuzzy model, Neuro
-
fuzzy syste
ms

Module 6:

Evolutionary Computing:

Introduction, gradient free optimization, genetic Algorithms: basic con
cept, search space, working
pri
nciple, encoding, decoding, fitness function, selection, cross over, mutation
, Particle swarm
optimization: basic pri
nciple, algorithm & flowchart

Module 7:

Application
s

of ANN, fuzzy systems & Evolutionary Computing to time series prediction, pattern
classification, control, communication engineering and biomedical engineering.

B
OOK:

1
.

“Neural network Design”
-

M.T.
H
agan, B. Demuth & M. Beale, Thomson Learning, 2002

2
.

“Neural Networks: A comprehensive Foundation”


Simon Haykin (Pearson education)

3
.

“Neuro
-
Fuzzy and Soft Computing”
-

J.S.R. J
ang, C. T. Sun and E. Mizutani, PHI, NewDelhi

4
.


Neural Networks: A Classro
o
m Approach


S. Kumar, T
ata Mc

graw Hill, 2004.

5. Lecturer Notes