Table of contents : Cover Table of Contents Title Page Copyright About the Author Preface 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 Exercises 2 Fuzzy Computing 2.1 Fuzzy Sets 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 3 Artificial Neural Network 3.1 Fundamentals of Artificial Neural Network (ANN) 3.2 Standard Activation Functions in Neural Networks 3.3 Basic Learning Rules in ANN 3.4 McCulloch–Pitts ANN Model 3.5 Feed-Forward Neural Network 3.6 Feedback Neural Network Exercises 4 Deep Learning 4.1 Introduction to Deep Learning 4.2 Classification of Deep Learning Techniques Exercises 5 Probabilistic Reasoning 5.1 Introduction to Probabilistic Reasoning 5.2 Four Perspectives on Probability 5.3 The Principles of Bayesian Inference 5.4 Belief Network and Markovian Network 5.5 Hidden Markov Model 5.6 Markov Decision Processes 5.7 Machine Learning and Probabilistic Models Exercises 6 Population-Based Algorithms 6.1 Introduction to Genetic Algorithms 6.2 Five Phases of Genetic Algorithms 6.3 How Genetic Algorithm Works? 6.4 Application Areas of Genetic Algorithms 6.5 Python Code for Implementing a Simple Genetic Algorithm 6.6 Introduction to Swarm Intelligence 6.7 Few Important Aspects of Swarm Intelligence 6.8 Swarm Intelligence Techniques Exercises 7 Rough Set Theory 7.1 The Pawlak Rough Set Model 7.2 Using Rough Sets for Information System 7.3 Decision Rules and Decision Tables 7.4 Application Areas of Rough Set Theory 7.5 Using ROSE Tool for RST Operations Exercises 8 Hybrid Systems 8.1 Introduction to Hybrid Systems 8.2 Neurogenetic Systems 8.3 Fuzzy-Neural Systems 8.4 Fuzzy-Genetic Systems 8.5 Hybrid Systems in Medical Devices Exercises Index End User License Agreement