Neuro-Fuzzy Architectures and Hybrid Learning

Danuta Rutkowska

ISBN 10: 379082500X / ISBN 13: 9783790825008

Edité par Physica-Verlag HD, 2010

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783790825008

Détails bibliographiques

• Titre: Neuro-Fuzzy Architectures and Hybrid Learning
• Éditeur: Physica-Verlag HD
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 379082500X
• ISBN à 13 chiffres: 9783790825008
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 17 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 17 millimètres de profondeur
• Poids: 464 grammes

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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.