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# Fuzzy Logic and Neural Networks

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### Overview

This course will start with a brief introduction to fuzzy sets. The differences between fuzzy sets and crisp sets will be identified. Various terms used in the fuzzy sets and the grammar of fuzzy sets will be discussed, in detail, with the help of some numerical examples. The working principles of two most popular applications of fuzzy sets, namely fuzzy reasoning and fuzzy clustering will be explained, and numerical examples will be solved. Fundamentals of neural networks and various learning methods will then be discussed. The principles of multi-layer feed forward neural network, radial basis function network, self-organizing map, counter-propagation neural network, recurrent neural network, deep learning neural network will be explained with appropriate numerical examples. The method of evolving optimized fuzzy reasoning tools, neural networks will be discussed with the help of some numerical examples. Two popular neuro-fuzzy systems will be explained and numerical examples will be solved. A summary of the course will be given at the end.INTENDED AUDIENCE :Students belonging to all disciplines of Engineering, Researchers and practicing Engineers can take this course.PRE-REQUISITES :NilINDUSTRY SUPPORT :RDCIS, Ranchi CMERI, Durgapur Reliance Industries, Mumbai C-DAC, Kolkata, and others

### Syllabus

Week 1: Introduction to Fuzzy Sets
Week 2: Introduction to Fuzzy Sets (contd.); Fuzzy reasoning
Week 3: Fuzzy reasoning (contd.); Fuzzy clustering
Week 4: Fuzzy clustering (contd.); Fundamentals of Neural Networks
Week 5: Multi-layer Feed-Forward Neural Network; Radial Basis FunctionNetwork
Week 6: Self-Organizing Map; Counter-Propagation Neural Network;Recurrent Neural Networks; Deep Learning Neural Network
Week 7: Genetic-Fuzzy system; Genetic-Neural System
Week 8: Neuro-Fuzzy System; Concepts of Soft Computing andComputational Intelligence; Summary of the Course

### Taught by

Prof. Dilip Kumar Pratihar

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