Soft computing in water resources engineering : artificial neural networks, fuzzy logic and genetic algorithms /
Tayfur, G.
Soft computing in water resources engineering : artificial neural networks, fuzzy logic and genetic algorithms / G. Tayfur. - Southampton ; Boston : WIT Press, c2012. - xvii,267 p. : ill. ; 25 cm.
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
9781845646363 (hbk.) 1845646363 (hbk.)
2011932561
GBB197358 bnb
015869749 Uk
Water resources development
Soft computing
627.04 / TAY
Soft computing in water resources engineering : artificial neural networks, fuzzy logic and genetic algorithms / G. Tayfur. - Southampton ; Boston : WIT Press, c2012. - xvii,267 p. : ill. ; 25 cm.
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
9781845646363 (hbk.) 1845646363 (hbk.)
2011932561
GBB197358 bnb
015869749 Uk
Water resources development
Soft computing
627.04 / TAY