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# Soft Computing Syllabus

This article is created to cover chapter-wise syllabus of soft computing. Let's start with its introduction.

## Introduction to Soft Computing

- Concept of computing systems
- Difference between Hard computing and Soft computing
- Characteristics of Soft computing
- Requirement of Soft computing
- Major Areas of Soft Computing
- Applications of Soft Computing

## Fuzzy logic

- Introduction, Classical Sets and Fuzzy Sets
- Classical Relations and Fuzzy Relations
- Membership Functions
- Fuzzy-to-Crisp Conversions, Fuzzy Arithmetic
- Classical Logic and Fuzzy Logic
- Fuzzy Rule-Based Systems
- Fuzzy Decision Making
- Fuzzy Classification

## Genetic Algorithms

- History of Genetic Algorithms (GA)
- Concept of "Genetics" and "Evolution" and its application to proablistic search techniques
- Basic GA framework and different GA architectures
- GA operators: Encoding, Crossover, Selection, Mutation, etc.
- Solving single-objective optimization problems using GAs

## Hybrid Systems

- Sequential Hybrid Systems
- Auxiliary Hybrid Systems
- Embedded Hybrid Systems
- Neuro-Fuzzy Hybrid Systems
- Neuro-Genetic Hybrid Systems
- Fuzzy-Genetic Hybrid Systems

## Multi-objective Optimization Problem (MOOP) Solving

- Concept of MOOPs and issues of solving them
- Multi-Objective Evolutionary Algorithm (MOEA)
- Non-Pareto approaches to solve MOOPs
- Pareto-based approaches to solve MOOPs
- Some applications with MOEAs

## Artificial Neural Networks

- What is Neural Network?
- Overview of Biological Neurons:
- Structure of biological neurons relevant to ANNs

- Fundamental Concepts of Artificial Neural Networks:
- Models of ANNs
- Feedforward & feedback networks
- Learning rules:
- Hebbian learning rule
- perception learning rule
- delta learning rule
- Widrow-Hoff learning rule
- correction learning rule
- Winner â€“lake all elarning rule

- Single layer Perception Classifier:
- Classification model
- Features & Decision regions
- Training & classification using discrete perceptron
- Algorithm
- Single layer continuous perceptron networks for linearly seperable classifications

- Multi-layer Feed forward Networks:
- Linearly non-seperable pattern classification
- Delta learning rule for multi-perceptron layer
- Generalized delta learning rule
- Error back-propagation training
- Learning factors

- Single layer feed back Networks:
- Basic Concepts
- Hopfield networks
- Training & Examples

- Associative memories:
- Linear Association
- Basic Concepts of recurrent Auto associative memory:
- Rentrieval algorithm
- Storage algorithm

- By directional associative memory
- Architecture
- Association encoding & decoding
- Stability
- Self organizing networks:
- Unsupervised learning of clusters
- Winner-take-all learning
- Recall mode
- Initialisation of weights
- Seperability limitations

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