Soft computing in water resources engineering : artificial neural networks, fuzzy logic and genetic algorithms / G. Tayfur.

Part 1 Artificial Neural Networks
Chapter 1 Introduction to Artificial Neural Networks
1.1 general View
1.2 biological Neuron
1.3 Artificial Neuron
1.4 Artificial neural Network
1.5 ANN Types
etc

Chapter 2 Artificial Neuron
2.1 Components of Artificial Neuron
2.2 Methods for computing Net Information
2.3 Activation Functions


Chapter 3 Network Training
3.1 Pre-Training procedures
3.2 Network Training

Chapter 4 Model Testing
4.1 De-standardization of Model output
4.2 Evaluating Model Performance
4.3 Over Training and Cross Training

Chapter 5 Model application in Water Resources Engineering
5.1 Prediction
5.2 Classification
5.3 Forecasting


Part II Introduction to Fuzzy Logic Algorithm

Chapter 6 Introduction to Fuzzy logic Algorithm
6.1 General View
6.2 Basic concept in Fuzzy Logic
6.3 Fuzzy Systems

Chapter 7 Fuzzy Membership Functions set operations and Fuzzy Relations
7.1 Fuzzy Membership Functions
7.2 Fuzzy Set Operations
7.3 Fuzzy Relations

Chapter 8 Constructing Fuzzy Model
8.1 Fuzzification
8.2 Fuzzy Rule Base
8.3 Fuzzy Inference Engine


Chapter 9 Fuzzy model Application in Water Resources
9.1 Introduction
9.2 TSS Prediction
9.3 Sheet Sediment Prediction
9.4 Peak Discharge Prediction
etc

Part III Genetic Algorithms

Chapter 10 Genetic Algorithms
10.1 Introduction
10.2 Basic Units of GA
10.3 GA Operations

Chapter 11 Variant of Genetic Algorithm
11.1 Variant of Genetic Algorithms

Chapter 12 Genetic Algorithm Model Applications in Water Resources Engineering
12.1 Ga Application Problems

References : p. 259 - 261 . _ Subject index : p. 263 - 267