CS6012 SOFT COMPUTING L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Learn the various soft computing frame works.
Be familiar with design of various neural networks.
Be exposed to fuzzy logic.
Learn genetic programming.
Be exposed to hybrid systems.
UNIT I INTRODUCTION (9)
Artificial neural network: Introduction, characteristics- learning methods – taxonomy – Evolution of
neural networks- basic models - important technologies - applications.
Fuzzy logic: Introduction - crisp sets- fuzzy sets - crisp relations and fuzzy relations: cartesian product
of relation - classical relation, fuzzy relations, tolerance and equivalence relations, non-iterative fuzzy
sets. Genetic algorithm- Introduction - biological background - traditional optimization and search
techniques - Genetic basic concepts.
UNIT II NEURAL NETWORKS (9)
McCulloch-Pitts neuron - linear separability - hebb network - supervised learning network: perceptron
networks - adaptive linear neuron, multiple adaptive linear neuron, BPN, RBF, TDNN- associative
memory network: auto-associative memory network, hetero-associative memory network, BAM,
hopfield networks, iterative autoassociative memory network & iterative associative memory network –unsupervised learning networks: Kohonen self organizing feature maps, LVQ – CP networks, ART
network.
UNIT III FUZZY LOGIC (9)
Membership functions: features, fuzzification, methods of membership value assignments-
Defuzzification: lambda cuts - methods - fuzzy arithmetic and fuzzy measures: fuzzy arithmetic -
extension principle - fuzzy measures - measures of fuzziness -fuzzy integrals - fuzzy rule base and
approximate reasoning : truth values and tables, fuzzy propositions, formation of rules-decomposition
of rules, aggregation of fuzzy rules, fuzzy reasoning-fuzzy inference systems-overview of fuzzy expert system-fuzzy decision making.
UNIT IV GENETIC ALGORITHM (9)
Genetic algorithm and search space - general genetic algorithm – operators - Generational cycle -
stopping condition – constraints - classification - genetic programming – multilevel optimization – real life problem- advances in GA.
UNIT V HYBRID SOFT COMPUTING TECHNIQUES & APPLICATIONS (9)
Neuro-fuzzy hybrid systems - genetic neuro hybrid systems - genetic fuzzy hybrid and fuzzy genetic
hybrid systems - simplified fuzzy ARTMAP - Applications: A fusion approach of multispectral images
with SAR, optimization of traveling salesman problem using genetic algorithm approach, soft
computing based hybrid fuzzy controllers.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the student should be able to:
Apply various soft computing frame works.
Design of various neural networks.
Use fuzzy logic.
Apply genetic programming.
Discuss hybrid soft computing.
TEXT BOOKS:
1. J.S.R.Jang, C.T. Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI / Pearson
Education 2004.
2. S.N.Sivanandam and S.N.Deepa, "Principles of Soft Computing", Wiley India Pvt Ltd, 2011.
REFERENCES:
1. S.Rajasekaran and G.A.Vijayalakshmi Pai, "Neural Networks, Fuzzy Logic and Genetic
Algorithm: Synthesis & Applications", Prentice-Hall of India Pvt. Ltd., 2006.
2. George J. Klir, Ute St. Clair, Bo Yuan, “Fuzzy Set Theory: Foundations and Applications”
Prentice Hall, 1997.
3. David E. Goldberg, “Genetic Algorithm in Search Optimization and Machine Learning” Pearson
Education India, 2013.
4. James A. Freeman, David M. Skapura, “Neural Networks Algorithms, Applications, and
Programming Techniques, Pearson Education India, 1991.
5. Simon Haykin, “Neural Networks Comprehensive Foundation” Second Edition, Pearson
Education, 2005.
3 0 0 3
OBJECTIVES:
The student should be made to:
Learn the various soft computing frame works.
Be familiar with design of various neural networks.
Be exposed to fuzzy logic.
Learn genetic programming.
Be exposed to hybrid systems.
UNIT I INTRODUCTION (9)
Artificial neural network: Introduction, characteristics- learning methods – taxonomy – Evolution of
neural networks- basic models - important technologies - applications.
Fuzzy logic: Introduction - crisp sets- fuzzy sets - crisp relations and fuzzy relations: cartesian product
of relation - classical relation, fuzzy relations, tolerance and equivalence relations, non-iterative fuzzy
sets. Genetic algorithm- Introduction - biological background - traditional optimization and search
techniques - Genetic basic concepts.
UNIT II NEURAL NETWORKS (9)
McCulloch-Pitts neuron - linear separability - hebb network - supervised learning network: perceptron
networks - adaptive linear neuron, multiple adaptive linear neuron, BPN, RBF, TDNN- associative
memory network: auto-associative memory network, hetero-associative memory network, BAM,
hopfield networks, iterative autoassociative memory network & iterative associative memory network –unsupervised learning networks: Kohonen self organizing feature maps, LVQ – CP networks, ART
network.
UNIT III FUZZY LOGIC (9)
Membership functions: features, fuzzification, methods of membership value assignments-
Defuzzification: lambda cuts - methods - fuzzy arithmetic and fuzzy measures: fuzzy arithmetic -
extension principle - fuzzy measures - measures of fuzziness -fuzzy integrals - fuzzy rule base and
approximate reasoning : truth values and tables, fuzzy propositions, formation of rules-decomposition
of rules, aggregation of fuzzy rules, fuzzy reasoning-fuzzy inference systems-overview of fuzzy expert system-fuzzy decision making.
UNIT IV GENETIC ALGORITHM (9)
Genetic algorithm and search space - general genetic algorithm – operators - Generational cycle -
stopping condition – constraints - classification - genetic programming – multilevel optimization – real life problem- advances in GA.
UNIT V HYBRID SOFT COMPUTING TECHNIQUES & APPLICATIONS (9)
Neuro-fuzzy hybrid systems - genetic neuro hybrid systems - genetic fuzzy hybrid and fuzzy genetic
hybrid systems - simplified fuzzy ARTMAP - Applications: A fusion approach of multispectral images
with SAR, optimization of traveling salesman problem using genetic algorithm approach, soft
computing based hybrid fuzzy controllers.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the student should be able to:
Apply various soft computing frame works.
Design of various neural networks.
Use fuzzy logic.
Apply genetic programming.
Discuss hybrid soft computing.
TEXT BOOKS:
1. J.S.R.Jang, C.T. Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI / Pearson
Education 2004.
2. S.N.Sivanandam and S.N.Deepa, "Principles of Soft Computing", Wiley India Pvt Ltd, 2011.
REFERENCES:
1. S.Rajasekaran and G.A.Vijayalakshmi Pai, "Neural Networks, Fuzzy Logic and Genetic
Algorithm: Synthesis & Applications", Prentice-Hall of India Pvt. Ltd., 2006.
2. George J. Klir, Ute St. Clair, Bo Yuan, “Fuzzy Set Theory: Foundations and Applications”
Prentice Hall, 1997.
3. David E. Goldberg, “Genetic Algorithm in Search Optimization and Machine Learning” Pearson
Education India, 2013.
4. James A. Freeman, David M. Skapura, “Neural Networks Algorithms, Applications, and
Programming Techniques, Pearson Education India, 1991.
5. Simon Haykin, “Neural Networks Comprehensive Foundation” Second Edition, Pearson
Education, 2005.
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