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 –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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