Soft Computing
24CSE7163 · S7 · 4 credits
Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.
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Why:Foundation module
Why:High exam relevance
Why:Core module
Why:Applied concepts
Why:Standard exam coverage
Module 01Introduction to Soft Computing and Neural Networksexpand
Concept of computing - soft computing vs. hard computing, Techniques, Applications, Characteristics, Hybrid computing. Artificial Neural Networks - Basic Concepts, The Biological Neuron, The Artificial Neuron. Basic models of artificial neural networks – Connections, Learning, Activation Functions. Linear separability, The XOR Problem.
Module 02Artificial Neural Networksexpand
Neural Network Architectures, Hebb Rule, Hebb Nets, Perceptrons - Training & Testing Algorithms, Delta Rule, Adaline, Madaline, The Backpropagation Algorithm - Training & Testing Algorithms
Module 03Introduction to Fuzzy Logicexpand
Fuzzy sets – properties, operations on fuzzy set. Fuzzy membership functions, Methods of membership value assignments – intuition, inference, Rank Ordering. Fuzzy relations– operations, Fuzzy Propositions. Fuzzy implications. Defuzzification– Lamda cuts, Defuzzification methods.
Module 04Evolutionary Computingexpand
Genetic Algorithms - Chromosomes, Population, GA Operators, GA Parameters, Convergence. Simulated Annealing (SA), Ant Colony Optimization, Particle Swam Optimization.
Module 05Advanced Search Techniquesexpand
Multi Objective Optimization Problem Formulation, The Pareto-optimal Front, Pareto-optimal Ranking, Multi-objective Fitness, Multi-objective GA Process.
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