Soft Computing for B.Tech 7th sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.<\/p>\n
The detailed syllabus for Soft Computing B.Tech (R13) seventhsem is as follows<\/p>\n
OBJECTIVES<\/strong>: The student should be made to:<\/p>\n UNIT I : INTRODUCTION<\/strong>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 [ 9 hours ] UNIT II : NEURAL NETWORKS<\/strong> \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0 \u00a0 [ 9 hours ] UNIT III : FUZZY LOGIC<\/strong> \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0 \u00a0 [ 9 hours ]\n
\nArtificial neural network: Introduction, characteristics- learning methods \u2013 taxonomy \u2013 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.<\/p>\n
\nMcCulloch-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 \u2013unsupervised learning networks: Kohonen self organizing feature maps, LVQ \u2013 CP networks, ART network.<\/p>\n
\nMembership 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.<\/p>\n