1. Fuzzy Logic
1.1. Brief Overview of Classical Logic
1.2. Elements of Fuzzy Logic
1.3. Semantic Analysis of Different Fuzzy Logics
1.4. Fuzzy Inference Rules and Approximate Reasoning
1.5. Experimental Selection of Appropriate Fuzzy Implications
2. Applications of Fuzzy Sets
2.1. Fuzzy Modeling
2.2. Fuzzy Decision Making
2.3. Pattern Analysis and Classification
2.4. Fuzzy Control Systems
2.5. Fuzzy Information Processing
2.5.1. Fuzzy information systems
2.5.2. Fuzzy data base and fuzzy operations in fuzzy data base management systems
2.5.3. Fuzzy information retrieval
2.6. Fuzzy Robotics
2.6.1. Fuzzy control system for a robot
2.6.2. Optimal path planning for a mobile intelligence robot with fuzzy resolution principle
3. Rough Sets
3.1 Basic rough set data analysis
3.1.2 Approximation quality
3.1.3 Information systems
3.1.4 Indiscernability relations
3.1.5 Feature selection
3.1.6 Discernability matrices and Boolean reasoning
3.2 Data discretisation
3.2.1 Classificatory discretisation
3.2.2 Discretisation of real valued attributes
3.3 Model selection
3.3.1 Dynamic reducts
3.3.2 Rough entropy measures
3.3.3 Entropy measures and approximation quality
4. Artificial Neural Networks
4.1 Basic architecture of Neural Nets
4.2 Feed-Forward Neural Networks
4.3 Recurrent Neural Networks
4.4 Time-Delay Neural Networks
4.5 Supervised Learning of Neural Networks
4.6 Reinforcement Learning
4.7 Application of ANN
6. Probabilistic Reasoning
6.1. Bayesian Approach
6.2. Dempster-Shafer Theory of Belief
6.3. Upper and Lower Prevision
6.4. Mixed Formalism Based Reasoning
7. Genetic Algorithms
7.1. Genetic Algorithms. Main Operators
7.2. Genetic Algorithm Based Optimization
7.3. Genetic Algorithm with Group Principle
7.4. Group Genetic Algorithms with Directed Mutation
8. Elements of Chaos Theory
8.1. Basic Concepts of Chaos Theory
8.2. Identification of Chaotic Movement of Systems
8.3. Bifurcation and Handling of Development of Chaos
8.4. Empirical Chaos
8.5. Chaotic Analysis of Petroleum Production Time Series
9. Neuro-Fuzzy Technology
9.1. Fuzzy Neural Networks and Their Learning
9.1.1. Fuzzy neural networks 301
9.1.2. General learning algorithm for fuzzy neural network
9.2. Architecture of Neuro-Fuzzy Systems
9.3. Generation of Fuzzy Rules and Membership Functions
9.4. Fuzzification and Defuzzification in Neuro-Fuzzy Systems
10. Combination of Genetic Algorithms with Neural Networks
10.2. Use of Genetic Algorithms for Neural Network Learning
11. Combination of Genetic Algorithms and Fuzzy Logic
11.1. GA-Based Method for Defining of Relational Matrix and Membership Functions
11.2. Design of Fuzzy Knowledge Base by Using Genetic Algorithms
11.3. Fuzzy Genetic Algorithms
11.3.1. Use of fuzzy approach in genetic algorithms for tuning the search directions
11.3.2. Fuzzy logic based genetic algorithms
11.4. Fuzzy-Genetic Modeling
12. Neuro-Fuzzy-Genetic Approach
12.1. Genetic Algorithms in Learning of Fuzzy Neural Networks
12.2. Genetic Algorithm Based Fuzzy Neural Networks Control System
12.3. References 383