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

• Κύριος συγγραφέας: Gorzalczany, Marian B. (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2002.
• Έκδοση: 1st ed. 2002.
• Σειρά: Studies in Fuzziness and Soft Computing, 86
• Θέματα: 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 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. 

 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.