Fuzzy and Neuro-Fuzzy Intelligent Systems

• Κύριοι συγγραφείς: Czogala, Ernest (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Leski, Jacek (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, 2000.
• Έκδοση: 1st ed. 2000.
• Σειρά: Studies in Fuzziness and Soft Computing, 47
• Θέματα: Artificial intelligence.; Information technology.; Business-Data processing.; Artificial Intelligence.; IT in Business.

Intelligence systems. We perfonn routine tasks on a daily basis, as for example: • recognition of faces of persons (also faces not seen for many years), • identification of dangerous situations during car driving, • deciding to buy or sell stock, • reading hand-written symbols, • discriminating betw...

Πίνακας περιεχομένων:

• 1 Classical sets and fuzzy sets Basic definitions and terminology
• 1.1 Classical sets
• 1.2 Fuzzy sets
• 1.3 Operations on fuzzy sets
• 1.4 Classification of t-norms and t-conorms
• 1.5 De Morgan triple and other properties of t- and s-norms
• 1.6 Parameterized t-, s-norms and negations
• 1.7 Fuzzy relations
• 1.8 Cylindrical extension and projection of fuzzy sets
• 1.9 Extension principle
• 1.10 Linguistic variable
• 1.11 Summary
• Bibliographical notes
• 2 Approximate reasoning
• 2.1 Interpretation of fuzzy conditional statement
• 2.2 An approach to axiomatic definition of fuzzy implication
• 2.3 Compositional rule of inference
• 2.4 Fuzzy reasoning
• 2.5 Canonical fuzzy if-then rule
• 2.6 Aggregation operation
• 2.7 Approximate reasoning using a fuzzy rule base
• 2.8 Approximate reasoning with singletons
• 2.9 Fuzzifiers and defuzzifiers
• 2.10 Equivalence of approximate reasoning results using different interpretations of if-then rules
• 2.11 Numerical results
• 2.12 Summary
• Bibliographical notes
• 3 Artificial neural networks
• 3.1 Introduction
• 3.2 Artificial neural networks topologies
• 3.3 Learning in artificial neural networks
• 3.4 Back-propagation learning rule
• 3.5 Modifications of the classic back-propagation method
• 3.6 Optimization methods in neural networks learning
• 3.7 Networks with output linearly depending on parameters
• 3.8 Global optimization methods
• 3.9 Summary
• Bibliographical notes
• 4 Unsupervised learning Clustering methods
• 4.1 Introduction
• 4.2 Self-organizing feature map
• 4.3 Vector quantization and learning vector quantization
• 4.4 An overview of clustering methods
• 4.5 Fuzzy clustering methods
• 4.6 A possibilistic approach to clustering
• 4.7 New generalized weighted conditional fuzzy c-means
• 4.8 Fuzzy learning vector quantization
• 4.9 Cluster validity
• 4.10 Summary
• Bibliographical notes
• 5 Fuzzy systems
• 5.1 Introduction
• 5.2 The Mamdani fuzzy systems
• 5.3 The Takagi-Sugeno-Kang fuzzy systems
• 5.4 Fuzzy systems with parameterized consequents
• 5.5 Summary
• Bibliographical notes
• 6 Neuro-fuzzy systems
• 6.1 Introduction
• 6.2 Artificial neural network based fuzzy inference system
• 6.3 Classifier based on neuro-fuzzy system
• 6.4 ANNBFIS optimization using deterministic annealing
• 6.5 Further investigations of neuro-fuzzy systems
• 6.6 Summary
• Bibliographical notes
• Appendix A: Artificial neural network based fuzzy inference systema MATLAB implementation
• Appendix B: Proof of classifier learning convergence
• 7 Applications of artificial neural network based fuzzy inference system
• 7.1 Introduction
• 7.2 Application to chaotic time series prediction
• 7.3 Application to ECG signal compression
• 7.4 Application to Ripley's synthetic two-class data classification
• 7.5 Application to the recognition of diabetes in Pima Indians
• 7.6 Application to the iris problem
• 7.7 Application to Monk's problems
• 7.8 Application to system identification
• 7.9 Application to control
• 7.10 Application to channel equalization
• 7.11 Summary
• Biographical notes
• References
• List of notations and abbreviations.