Computational Intelligence Systems and Applications Neuro-Fuzzy and Fuzzy Neural Synergisms /

• Main Author: Gorzalczany, Marian B. (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2002.
• Edition: 1st ed. 2002.
• Series: Studies in Fuzziness and Soft Computing, 86
• Subjects: Artificial intelligence.; Computer mathematics.; Artificial Intelligence.; Computational Mathematics and Numerical Analysis.; Computational Science and Engineering.

This book presents new concepts and implementations of Computational Intelligence (CI) systems (based on neuro-fuzzy and fuzzy neural synergisms) and a broad comparative analysis with the best-known existing neuro-fuzzy systems as well as with systems representing other knowledge-discovery technique...

Table of Contents:

• 1 Introduction
• 1.1 A general concept of computational intelligence
• 1.2 The building blocks of computational intelligence systems
• 1.3 Objectives and scope of this book
• 2 Elements of the theory of fuzzy sets
• 2.1 Basic notions, operations on fuzzy sets, and fuzzy relations
• 2.2 Fuzzy inference systems
• 3 Essentials of artificial neural networks
• 3.1 Processing elements and multilayer perceptrons
• 3.2 Radial basis function networks
• 4 Brief introduction to genetic algorithms
• 4.1 Basic components of genetic algorithms
• 4.2 Theoretical introduction to genetic computing
• 5 Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems
• 5.1 Artificial intelligence versus computational intelligence
• 5.2 Designing computational intelligence systems
• 5.3 Selected neuro-fuzzy systems
• 6 Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data
• 6.1 Synthesizing rule-based knowledge from data - statement of the problem
• 6.2 Neuro-fuzzy system in learning mode - problem of knowledge acquisition
• 6.3 Neuro-fuzzy system in inference mode - approximate inference engine
• 6.4 Learning techniques
• 6.5 A numerical example of synthesizing rule-based knowledge from data - modelling the Mackey-Glass chaotic time series
• 6.6 Synthesizing rule-based knowledge from "fish data"
• 7 Rule-based neuro-fuzzy modelling of dynamic systems and designing of controllers
• 7.1 System identification - statement of the problem and its general solution in the framework of neuro-fuzzy methodology
• 7.2 Rule-based neuro-fuzzy modelling of an industrial gas furnace system
• 7.3 Designing the neuro-fuzzy controller for a simulated backing up of a truck
• 8 Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support
• 8.1 Designing the classifier from data - statement of the problem
• 8.2 Learning mode of neuro-fuzzy classifier
• 8.3 Inference (decision making) mode of neuro-fuzzy classifier
• 8.4 Neuro-fuzzy decision support system for diagnosing breast cancer
• 8.5 Neuro-fuzzy-genetic decision support system for the glass identification problem (forensic science)
• 8.6 Neuro-fuzzy-genetic decision support system for determining the age of abalone (marine biology)
• 9 Fuzzy neural network for system modelling and control
• 9.1 Learning mode of the network
• 9.2 Inference mode of the network
• 9.3 Fuzzy neural modelling of dynamic systems (an industrial gas furnace system)
• 9.4 Fuzzy neural controller
• 10 Fuzzy neural classifier
• 10.1 Learning and inference modes of the classifier
• 10.2 Fuzzy neural classifier for diagnosis of surgical cases in the domain of equine colic
• A Appendices
• A.1.1 Inputs
• A.1.2 Output
• A.2.1 Inputs
• A.2.2 Outputs - set of two class labels
• A.3.1 Inputs
• A.3.2 Outputs - set of two class labels
• A.4.1 Inputs
• A.4.2 Outputs - set of three class labels
• A.5.1 Inputs
• A.5.2 Outputs - three sets of class labels
• References.