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1.1 Soft Computing
1.1.1 Fuzzy Logic
1.1.2 Neural Networks
1.1.3 Probabilistic Reasoning
2. Fuzzy Logic Systems .
2.1 Basic Concepts
2.1.1 Set-Theoretical Operations and Basic Definitions
2.1.2 Fuzzy Relations .
2.1.3 The Extension Principle .
2.1.4 Approximate Reasoning
2.1.5 Fuzzy Rules .
2.1.6 Fuzzy Inference
2.1.7 Fuzzifier and Defuzzifier .
2.1.8 The General Relation between Probability and Fuzziness .
2.2 Different Fuzzy Systems .
2.2.1 Takagi and Sugeno’s Fuzzy System .
2.2.2 Mendel-Wang’s Fuzzy System
2.2.3 Kosko’s Standard Additive Model (SAM)
2.3 Approximation Capability .
2.4 Different Interpretations of Fuzzy Sets
2.5 Different Ways to form Fuzzy Sets
3. Neural Networks .
3.1 Basic Concepts
3.1.1 Single-layer Feedforward Networks
3.2 Multilayer Perceptron
3.3 Functional Link Network
3.4 Radial Basis Function Network
3.5 Neuro-Fuzzy Networks
3.5.1 Different Neurofuzzy Approaches
3.7 Approximation Capability
4. Unified Form of Soft Computing Methods .
4.1 Introduction .
4.2 Interpolation Networks .
4.3 Models for Non-linear Mapping .
4.4 Posterior Probability in FLS and Neural Networks
4.5 Basis Function Selection
5. Training of Soft Computing Methods
5.1 The Basics
5.2 Network Mapping
5.3 Regularization .
5.4 The Bias/Variance Tradeoff .
5.5 Training Methods in Nutshell
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