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

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

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 –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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