Neuro-Fuzzy Architectures and Hybrid Learning (Paperback)

Danuta Rutkowska

ISBN 10: 379082500X / ISBN 13: 9783790825008

Edité par Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg, 2010

A propos de cet article

Paperback. The main idea of this book is to present novel connectionist architectures of neuro-fuzzy systems, especially those based on the logical approach to fuzzy inference. In addition, hybrid learning methods are proposed to train the networks. The neuro-fuzzy architectures plus hybrid learning are considered as intelligent systems within the framework of computational and artificial intelligence. The book also provides an overview of fuzzy sets and systems, neural networks, learning algorithms (including genetic algorithms and clustering methods), as well as expert systems and perception-based systems which incorporates computing with words. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

N° de réf. du vendeur 9783790825008

Détails bibliographiques

• Titre: Neuro-Fuzzy Architectures and Hybrid Learning (Paperback)
• Éditeur: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 379082500X
• ISBN à 13 chiffres: 9783790825008
• Reliure: Paperback
• Édition: Edition originale
• État de l'article: new

A propos de ce titre

Synopsis

The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe- matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ- ence of the human mind as a role model is clearly visible in the methodolo- gies which have emerged, mainly during the past two decades, for the con- ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively provide a body of concepts and techniques for automation of reasoning and decision-making in an environment of imprecision, uncertainty and partial truth.