Syllabus
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Course Code: Elective-V PE-CS-D407 Course Name: Soft Computing |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Artificial Neural Networks Fundamentals of Biological Neural Network and Artificial Neural Network, Evolution of Neural Networks, Learning – supervised, unsupervised and reinforcement, Terminologies – weights, bias, threshold, learning rate, Hebb Network, Perceptron Networks, Backpropagation Network, Associative Memory Network, Hopfield Networks, Counter propagation Networks, Adaptive Resonance Theory Network, Optical Neural Networks, Applications of Neural Networks. |
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| 2 | Fuzzy Systems Introduction to Fuzzy Logic, Classical Sets and Fuzzy Sets, Operations on Crisp Sets and Fuzzy Sets, Classical Relation and Fuzzy Relations, Membership Functions, Methods of Membership Value Assignments, Fuzzy Arithmetic and Fuzzy Measures, Fuzzy Rule Base and Approximate Reasoning, Fuzzy Decision Making, Fuzzy Logic Control Systems, Applications of Fuzzy Logic based systems. |
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| 3 | Nature-Inspired Algorithms Introduction to Nature-Inspired algorithms, Swarm Intelligence, Genetic Algorithm (GA), Operators in Genetic Algorithm – Encoding, Selection, Crossover, Mutation, Stopping Condition for GA, Differential Evolution (DE) Algorithm, Particle Swarm Optimization (PSO) Algorithm, Ant Bee Colony (ABC) Algorithm, Flower Pollination Algorithm, Solution of Real World Problems using Nature-Inspired Algorithms. |
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| 4 | Optimization Objective of Optimization, Single-objective Optimization, Multi-objective Optimization, Pareto-optimal solutions, Travelling Salesman Problem solution using any optimization technique, Engineering problems solution using any Soft Computing approach, Architecture of Neuro-Fuzzy Systems and Genetic Neuro-Fuzzy Hybrid Systems, Applications of Soft Computing. |
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