Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic AlgorithmsDa Ruan Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are original contributions by leading researchers written exclusively for this volume. This book reviews important concepts and models, and focuses on specific methodologies common to fuzzy systems, neural networks and evolutionary computation. The emphasis is on development of cooperative models of hybrid systems. Included are applications related to intelligent data analysis, process analysis, intelligent adaptive information systems, systems identification, nonlinear systems, power and water system design, and many others. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms provides researchers and engineers with up-to-date coverage of new results, methodologies and applications for building intelligent systems capable of solving large-scale problems. |
Contents
| 3 | |
What are genetic algorithms | 17 |
Models and applications of cooperative systems | 24 |
REFERENCES | 31 |
network parameters | 40 |
NOVEL NEURAL ALGORITHMS FOR SOLVING | 59 |
METHODS FOR SIMPLIFICATION | 91 |
A NEW APPROACH OF NEUROFUZZY LEARNING | 109 |
APPLICATIONS OF INTELLIGENT TECHNIQUES | 190 |
NEUROFUZZYCHAOS ENGINEERING | 209 |
A SEQUENTIAL TRAINING STRATEGY | 231 |
NONLINEAR SYSTEMS AND SYSTEM | 253 |
NONLINEAR SYSTEM IDENTIFICATION WITH | 282 |
A GENETIC ALGORITHM FOR MIXEDINTEGER | 311 |
SOFT COMPUTING BASED SIGNAL PREDICTION | 331 |
| 353 | |
Other editions - View all
Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms Da Ruan Limited preview - 1997 |
Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms Da Ruan No preview available - 2012 |
Common terms and phrases
adaptation analysis application approach architecture Babuška backpropagation chaotic computational configuration conventional neural network convergence data points data set defined developed dynamic error evolutionary evolutionary algorithm example extended fuzzy Figure FuNN furnace fuzzy c-means algorithm fuzzy clustering fuzzy inference fuzzy logic fuzzy measure fuzzy model fuzzy neural network fuzzy partition fuzzy reasoning fuzzy relation equations fuzzy rules fuzzy sets fuzzy system Gaussian genetic algorithm genetic programming heaters hidden layer hidden neurons IEEE initial input domain input variables intelligent iteration Kaymak linear models LOLIMOT Mahalanobis distance matrix maximum solution membership functions mutation node vector noise nonlinear number of rules obtained offspring optimal optimisation output layer parameters parent patterns performance prediction problem Proc proposed RBF filter recurrent neural networks rule consequents selected signal simulation Step strategy structure system identification t-norm techniques temperature tion training data validity Wkj(t