Principles of Soft Computing Using Python Programming

Name: Principles of Soft Computing Using Python Programming | Learn How to Deploy Soft Computing Models in Real World Applications
Brand: Wiley
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Learn How to Deploy Soft Computing Models in Real World Applications

Gypsy Nandi(Autor*in)

Wiley (Verlag)

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Erschienen am 28. November 2023

352 Seiten

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978-1-394-17314-3 (ISBN)

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Inhalt

Cover
Title Page
Copyright
Contents
About the Author
Preface
Chapter 1 Fundamentals of Soft Computing
1.1 Introduction to Soft Computing
1.2 Soft Computing versus Hard Computing
1.3 Characteristics of Soft Computing
1.4 Components of Soft Computing
1.4.1 Fuzzy Computing
1.4.2 Neural Network
1.4.3 Evolutionary Computing
1.4.4 Machine Learning
1.4.5 Other Techniques of Soft Computing
Exercises
Chapter 2 Fuzzy Computing
2.1 Fuzzy Sets
2.1.1 Features of Fuzzy Membership Functions
2.2 Fuzzy Set Operations
2.3 Fuzzy Set Properties
2.4 Binary Fuzzy Relation
2.5 Fuzzy Membership Functions
2.6 Methods of Membership Value Assignments
2.7 Fuzzification vs. Defuzzification
2.8 Fuzzy c-Means
Exercises
Chapter 3 Artificial Neural Network
3.1 Fundamentals of Artificial Neural Network (ANN)
3.2 Standard Activation Functions in Neural Networks
3.2.1 Binary Step Activation Function
3.2.2 Linear Activation Function
3.2.3 Sigmoid/Logistic Activation Function
3.2.4 ReLU Activation Function
3.2.5 Tanh Activation Function
3.2.6 Leaky ReLU Activation Function
3.2.7 SoftMax Activation Function
3.3 Basic Learning Rules in ANN
3.3.1 Hebbian Learning Rule
3.3.2 Perceptron Learning Rule
3.3.3 Delta Learning Rule
3.3.4 Correlation Learning Rule
3.3.5 Competitive Learning Rule
3.3.6 Outstar Learning Rule
3.4 McCulloch-Pitts ANN Model
3.5 Feed-Forward Neural Network
3.5.1 Single-Layer Perceptron
3.5.2 Multilayer Perceptron
3.5.3 Radial Basis Function Network
3.6 Feedback Neural Network
3.6.1 Self-Organizing Map (SOM)
3.6.2 Hopfield Neural Network (HNN)
Exercises
Chapter 4 Deep Learning
4.1 Introduction to Deep Learning
4.2 Classification of Deep Learning Techniques
4.2.1 Convolutional Neural Networks
4.2.2 Recurrent Neural Network (RNN)
4.2.3 Generative Adversarial Network (GAN)
4.2.4 Autoencoders
Exercises
Chapter 5 Probabilistic Reasoning
5.1 Introduction to Probabilistic Reasoning
5.1.1 Random Experiment
5.1.2 Random Variables
5.1.3 Independence
5.1.4 Sample Space
5.1.5 Odds and Risks
5.1.6 Expected Values
5.2 Four Perspectives on Probability
5.2.1 The Classical Approach
5.2.2 The Empirical Approach
5.2.3 The Subjective Approach
5.2.4 The Axiomatic Approach
5.3 The Principles of Bayesian Inference
5.4 Belief Network and Markovian Network
5.4.1 Syntax and Semantics
5.4.1.1 Belief Network
5.4.1.2 Markovian Network
5.4.2 Conditional Independence
5.4.3 Learning Methods of the Networks
5.5 Hidden Markov Model
5.6 Markov Decision Processes
5.7 Machine Learning and Probabilistic Models
Exercises
Chapter 6 Population-Based Algorithms
6.1 Introduction to Genetic Algorithms
6.2 Five Phases of Genetic Algorithms
6.2.1 Population Initialization
6.2.2 Fitness Function Calculation (Evaluation)
6.2.3 Parent Selection
6.2.4 Crossover
6.2.5 Mutation
6.3 How Genetic Algorithm Works?
6.4 Application Areas of Genetic Algorithms
6.4.1 Using GA in Travelling Salesman Problem
6.4.2 Using GA in Vehicle Routing Problem
6.5 Python Code for Implementing a Simple Genetic Algorithm
6.6 Introduction to Swarm Intelligence
6.7 Few Important Aspects of Swarm Intelligence
6.7.1 Collective Sorting
6.7.2 Foraging Behavior
6.7.3 Stigmergy
6.7.4 Division of Labor
6.7.5 Collective Transport
6.7.6 Self-Organization
6.8 Swarm Intelligence Techniques
6.8.1 Ant Colony Optimization
6.8.1.1 How ACO Technique Works?
6.8.1.2 Applying ACO to Optimization Problems
6.8.1.3 Using ACO in Travelling Salesman Problem
6.8.1.4 Python Code for Implementing ACO in TSP
6.8.2 Particle Swarm Optimization
6.8.2.1 How PSO Technique Works?
6.8.2.2 Applying PSO to Optimization Problems
6.8.2.3 Using PSO in Job-Shop Scheduling Problem
6.8.2.4 Python Code for Implementing PSO
Exercises
Chapter 7 Rough Set Theory
7.1 The Pawlak Rough Set Model
7.1.1 Basic Terms in Pawlak Rough Set Model
7.1.2 Measures of Rough Set Approximations
7.2 Using Rough Sets for Information System
7.3 Decision Rules and Decision Tables
7.3.1 Parameters of Decision Tables
7.3.1.1 Consistency Factor
7.3.1.2 Support and Strength
7.3.1.3 Certainty Factor
7.3.1.4 Coverage Factor
7.3.2 Probabilistic Properties of Decision Tables
7.4 Application Areas of Rough Set Theory
7.4.1 Classification
7.4.2 Clustering
7.4.3 Medical Diagnosis
7.4.4 Image Processing
7.4.5 Speech Analysis
7.5 Using ROSE Tool for RST Operations
7.5.1 Attribute Discretization
7.5.2 Finding Lower and Upper Approximations
Exercises
Chapter 8 Hybrid Systems
8.1 Introduction to Hybrid Systems
8.2 Neurogenetic Systems
8.2.1 GA-based Weight Determination of Multilayer Feed-forward Net
8.2.2 Neuroevolution of Augmenting Topologies (NEAT)
8.3 Fuzzy-Neural Systems
8.3.1 Fuzzy Neurons
8.3.2 Adaptive Neuro-fuzzy Inference System (ANFIS)
8.4 Fuzzy-Genetic Systems
8.5 Hybrid Systems in Medical Devices
Exercises
Index
EULA

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