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Beschreibung
Neural networks have a learning capability but analysis of a trained network is difficult. On the other hand, extraction of fuzzy rules is difficult but once they have been extracted, it is relatively easy to analyze the fuzzy system. This book solves the above problems by developing new learning paradigms and architectures for neural networks and fuzzy systems.
The book consists of two parts:… Pattern Classification and Function Approximation. In the first part, based on the synthesis principle of the neural-network classifier: A new learning paradigm is discussed and classification performance and training time of the new paradigm for several real-world data sets are compared with those of the widely-used back-propagation algorithm; Fuzzy classifiers of different architectures based on fuzzy rules can be defined with hyperbox, polyhedral, or ellipsoidal regions. The book discusses the unified approach for training these fuzzy classifiers; The performance of the newly-developed fuzzy classifiers and the conventional classifiers such as nearest-neighbor classifiers and support vector machines are evaluated using several real-world data sets and their advantages and disadvantages are clarified.
In the second part: Function approximation is discussed extending the discussions in the first part; Performance of the function approximators is compared.
This book is aimed primarily at researchers and practitioners in the field of artificial intelligence and neural networks. 106,99 €
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Technische Daten
Erscheinungsdatum
04.10.2012
Sprache
Englisch
EAN
9781447110774
Herausgeber
Springer London
Sonderedition
Nein
Autor
Shigeo Abe
Seitenanzahl
327
Auflage
1
Einbandart
Broschiert
Buch Untertitel
Neuro-fuzzy Methods and Their Comparison
Schlagwörter
Fuzzy, Fuzzy function approximation, Pattern classification, Performance, Support Vector Machine, algorithms, architecture, artificial intelligence, classification, function approximation, fuzzy classifiers, fuzzy system, learning, multilayer neural networks, complexity
Thema-Inhalt
UYQ - Künstliche Intelligenz
TBJ - Mathematik für Ingenieure
GPFC - Kybernetik und Systemtheorie
UYQP - Mustererkennung
Höhe
235 mm
Breite
15.5 cm
Hersteller: Springer Nature Customer Service Center GmbH, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com
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