Ingénierie
Soft Computing: Fundamentals & Pratique Approchees
Description
This book introduces le fundamental concepts de different soft computing techniques that are widely used en a variety de optimization et classification problems. Topics covered include Fuzzy computing, Neural Network, Deep learning, Population based algorithms, Rough sets, Soft computing techniques en Intrusion Detection Systèmes et Soft Computing techniques en Données Mining. Le content discusses le fundamentals together avec how le soft computing techniques can be implemented using various standard libraries et tools de le Python programming language et le ROSE tool. Readers avec previous knowledge de python programming will find easy to understand le program examples presented en le chapters. Each chapter contains numerous examples et case study that explains le important concepts. Appropriate number de questions is presented at le end de each chapter pour self assessing le conceptual understanding. Le references presented at le end de every chapter will help le readers to explore more sur a given topic.
Table des matières
À propos de le Authors – Acknowledgment – Preface – Key Features de le Book – 1. Principes fondamentaux de Soft Computing 1.1. Introduction à Soft Computing 1.2. Soft computing versus Hard Computing 1.3. Soft computing Characteristics 1.4. Components de 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 de Soft Computing; References; Exercises – 2. Fuzzy Computing 2.1. Fuzzy Set 2.2. Fuzzy Set Operations 2.3. Fuzzy Set Properties 2.4. Fuzzy Membership Functions 2.5. Defuzzification 2.6. Le Butterfly Classification Problem 2.7. Fuzzy c-Means 2.8. Applications de Fuzzy Logic sur Different Product Développement 2.8.1. Contrôle de a Model Car-Like Vehicle 2.8.2. Driving a Car-Like Vehicle 2.8.3. Fuzzy Logic en Washing Machine 2.9. Pratique Approche de Fuzzy Using Python; Exercises; References – 3. Artificial Neural Networks 3.1. Introduction à Artificial Neural Network 3.1.1. McCulloh-Pitts Neuron Model 3.1.2. Le Perceptron 3.1.3. Types de Transfer Function 3.1.3.1. Hard Limit Transfer Function 3.1.3.2. Linear Transfer Function 3.1.3.3. Log-Simoid Transfer Function 3.1.3.4. Different Other Transfer Functions 3.1.4. Perceptron Learning et Learning Rate 3.1.5. Perceptron Algorithm 3.1.6. Pattern Classification 3.1.7. Gradient Descent Rule 3.2. Multilayer Perceptron 3.2.1. Preliminaries de Multilayer Neural Network 3.2.2. Multilayer Perceptron Algorithm 3.2.3. Backpropagation Training 3.2.3.1. Conception Procedure de le Algorithm 3.2.3.2. Batch Learning 3.2.3.3. Online Learning 3.2.4. Cross Validation et Generalization 3.2.5. Generalization 3.3. Self-Organizing Map 3.4. ANN Implementation en Python 3.4.1. Significance de Bias 3.4.2. Neural Network Conception using Python; References; Exercises – 4. Deep Learning 4.1. Introduction à Deep Learning 4.2. Deep Learning Primitives 4.2.1. Soft max Function 4.2.2. Sigmoid, Tanh et ReLU Neurons 4.2.3. Functions et Gradient Descent 4.2.4. Linear/Logistic Regression 4.3. Feedforward Network 4.4. Convolutional Neural Network 4.5. Recurrent Neural Network; Exercises; References – 5. Population Based Algorithms 5.1. Introduction à Génétique Algorithm 5.2. Five Phases de Génétique Algorithm 5.2.1. Population Initialization 5.2.2. Fitness Function Calculation (Evaluation) 5.2.3. Parent Selection 5.2.4. Crossover 5.2.5. Mutation 5.3. How Génétique Algorithm Works? 5.4. Application Areas de Génétique Algorithm (GA) 5.4.1. Using GUn en Travelling Salesman Problem 5.4.2. Using GUn en Vehicle Routing Problem 5.5. Python Code pour Implementing a Simple GUn 5.6. Introduction à Swarm Intelligence 5.7. Few Important Aspects de Swarm Intelligence 5.7.1. Collective Sorting 5.7.2. Foraging Behaviour 5.7.3. Stigmergy 5.7.4. Division de Labour 5.7.5. Collective Transport 5.7.6. Self-Organization 5.8. Swarm Intelligence Techniques 5.8.1. Ant Colony Optimization (ACO) 5.8.1.1. How ACO Technique Works? 5.8.1.2. Applying ACO to Optimization Problems 5.8.1.3 Using ACO en Travelling Salesman Problem (TSP) 5.8.1.4 Python Code pour Implementing ACO en TSP 5.8.2. Particle Swarm Optimization (PSO) 5.8.2.1. How PSO Technique Works? 5.8.2.2. Applying PSO to Optimization Problems 5.8.2.3. Using PSO en Job-Shop Scheduling Problem 5.8.2.4. Python Code pour Implementing PSO; Exercises; References – Rough Sets 6.1. Le Pawlak Rough Set Model 6.1.1 Basic Terms en Pawlak Rough Set Model 6.1.2 Measures de Rough Set Approximations 6.2. Using Rough Sets pour Information Système 6.3. Decision Rules et Decision Tables 6.3.1. Parameters de Decision Tables 6.3.1.1. Consistency Factor 6.3.1.2. Support et Strength 6.3.1.3. Certainty Factor 6.3.1.4. Coverage Factor 6.3.2. Probabilistic Properties de Decision Tables 6.4. Application Areas de Rough Set Théorie 6.4.1. Classification 6.4.2. Clustering 6.4.3. Medical Diagnosis 6.4.4. Image Processing 6.4.5. Speech Analyse 6.5. Using ROSE Tool pour RST Operations 6.5.1. Attribute Discretization 6.5.2. Finding Lower et Upper Approximations; Exercises; References – 7. Soft Computing Techniques et IDS 7.1. Génétique Algorithm et Intrusion Detection Systèmes (IDS) 7.2. Deep Learning et IDS 7.3. Recurrent Neural Network et IDS 7.4. Network Intrusion Detection using Rough Sets et KNN 7.5. Fuzzy Logic et IDS 7.6. IDS avec Génétique Algorithm et Fuzzy Logic 7.7. Feature Selection using Ant Colony Optimization 7.8. Decision Tree et IDS 7.9 Basic Python Code Programs pour Performing Simple Network Stuffs; Exercises; References – 8. Soft Computing Techniques en Données Mining 8.1. Feature Selection using Rough Set Théorie 8.2. Rule Induction using Rough Set Théorie 8.3. Données Preprocessing pour Données Mining 8.4. Hierarchical Clustering 8.5. Decision Tree Classification 8.6. KNN Classification 8.7. Decision Tree Regression 8.8. Random Forest Regression; Exercises; References – Subject Index – Appendix.