CS6012 SOFT COMPUTING
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Subject Informations :
University : Anna University
Department : B.E. COMPUTER SCIENCE AND ENGINEERING
Semester : 08 th sem
Year : 04 th year
Regulation : R2013
Subject Code : CS6012
Subject Name : SOFT COMPUTING
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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
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
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
Membership functions: features, fuzzification, methods of membership value assignmentsDefuzzification: 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
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
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.
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