CORE

Introduction to Soft Computing

24CSO724 · S7 · 4 credits

Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.

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Modules
Module 01Introduction to Soft Computing and Neural Networksexpand

Introduction to Soft Computing, Difference between Hard Computing and soft Computing, Applications of Soft Computing, Artificial Neurons Vs Biological Neurons, Basic models of artificial neural networks – Connections, Learning, Activation Functions, Important terminologies of ANN

Module 02Artificial Neural Networksexpand

Perceptron Network, Preceptron learning rule, Perceptron for classification and its limitations, Architectures of multilayer feed-forward neural networks, Backpropagation - Training & Testing algorithms, Limitations of MLP.

Module 03Supervised and Unsupervised Learningexpand

Hebb’s learning rule for competitive learning, Hebb network, Kohonen’s self- organizing map and network topology, applications of SOM, Hopfield network and its topology, Boltzman Machines, Adaptive Resonance Theory.

Module 04Fuzzy Set Theoryexpand

Fuzzy sets – properties, operations on fuzzy set. Fuzzy membership functions, Methods of membership value assignments – intuition, inference, Rank Ordering, Fuzzy relations– operations on fuzzy relation. Defuzzification– Lamda cuts, Defuzzification methods.

Module 05Fuzzy Inference System and Genetic Algorithmexpand

Fuzzy Inference Systems - Mamdani and Sugeno types, Fuzzy Logic Controller, Concepts of genetic algorithm, Operators in genetic algorithm - coding, selection, cross over, mutation, Stopping condition for genetic algorithm.

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