Marian B. Gorzalczany

Computational Intelligence Systems and Applications (eBook, PDF)

Neuro-Fuzzy and Fuzzy Neural Synergisms

• Format: PDF

Produktbeschreibung

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 techniques such as rough sets, decision trees, regression trees, probabilistic rule induction etc. This presentation is preceded by a discussion of the main directions of synthesizing fuzzy sets, artificial neural networks and genetic algorithms in the framework of designing CI systems. In order to keep the book self-contained, introductions to the basic concepts of fuzzy systems, artificial neural networks and genetic algorithms are given. This book is intended for researchers and practitioners in AI/CI fields and for students of computer science or neighbouring areas.

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Produktdetails

• Verlag: Physica-Verlag HD
• Seitenzahl: 364
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783790818017
• Artikelnr.: 53209162

Inhaltsangabe

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.- 8Neuro-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.