Soft Computing Notes Download Pdf

 

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This book gives an introduction to Soft Computing, which aims to exploit tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve close resemblance with human like decision making. What is soft computing Techniques used in soft computing 2 What is Soft Computing? (adapted from L.A. Zadeh) • Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. B.Tech Course on Soft Computing, lecture notes in pdf format. For Slides, click on right side Buttons or Topics. Internet Explorer users, pl allow ActiveX Control.for blocked content, pdf files.

This book gives an introduction to Soft Computing, which aims to exploit tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve close resemblance with human like decision making. Download notes for Soft Computing Note PDF download and free reading study material. Reading online is free Read Now. Download in Android App for free. Home » » Soft Computing Seminar abstract pdf Soft Computing Seminar abstract pdf. Soft Computing became a formal Computer Science area of study in the early 1990's.Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities. To download PRINCIPLES OF SOFT COMPUTING SIVANANDAM DEEPA PDF, click on the Download button. The various neural network concepts are explained with examples, highlighting the difference between various architectures.

Soft Computing Notes Download Pdf Download

Notes
  1. Chapter 1: Introduction
  2. Chapter 2: Fuzzy Sets
    1. 2.1 Crisp Sets: A Review
    2. 2.2 Fuzzy Sets
    3. 2.3 Fuzzy Membership Functions
    4. 2.5 Fuzzy Relations
    5. 2.6 Fuzzy Extension Principle
  3. Chapter 3: Fuzzy Logic
    1. 3.1 Crisp Logic: A Review
    2. 3.2 Fuzzy Logic Basics
    3. 3.4 Fuzzy Rules
    4. 3.5 Fuzzy Reasoning
  4. Chapter 4: Fuzzy Inference Systems
    1. 4.6 Defuzzification of the Resultant Aggregate Fuzzy Set
    2. 4.7 Fuzzy Controllers
  5. Chapter 5: Rough Sets
  6. Chapter 6: Artificial Neural Networks: Basic Concepts
    1. 6.1 Introduction
    2. 6.2 Computation in Terms of Patterns
    3. 6.4 The Perceptron
    4. 6.5 Neural Network Architectures
    5. 6.6 Activation Functions
    6. 6.7 Learning by Neural Nets
  7. Chapter 7: Pattern Classifiers
  8. Chapter 8: Pattern Associators
    1. 8.1 Auto-associative Nets
    2. 8.2 Hetero-associative Nets
    3. 8.3 Hopfield Networks
    4. 8.4 Bidirectional Associative Memory
  9. Chapter 9: Competitive Neural Nets
    1. 9.1 The MAXNET
    2. 9.2 Kohonen’s Self-organizing Map (SOM)
    3. 9.3 Learning Vector Quantization (LVQ)
    4. 9.4 Adaptive Resonance Theory (ART)
  10. Chapter 10: Backpropagation
    1. 10.1 Multi-layer Feedforward Net
    2. 10.3 The Backpropagation Algorithm
  11. Chapter 11: Elementary Search Techniques
    1. 11.2 State Space Search
    2. 11.3 Exhaustive Search
    3. 11.4 Heuristic Search
  12. Chapter 12: Advanced Search Strategies
    1. 12.1 Natural Evolution: A Brief Review
    2. 12.2 Genetic Algorithms (GAs)
    3. 12.3 Multi-objective Genetic Algorithms
  13. Chapter 13: Hybrid Systems
    1. 13.1 Neuro-genetic Systems
    2. 13.2 Fuzzy-neural Systems

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