Computational Intelligence Systems and Applications Neuro-Fuzzy and Fuzzy Neural Synergisms
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...
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| Format: | eBook |
| Language: | English |
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Heidelberg
Physica
2002, 2002
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| Edition: | 1st ed. 2002 |
| Series: | Studies in Fuzziness and Soft Computing
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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
- 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
- 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