Computational Intelligence Systems and Applications (Hardcover)

Marian B. Gorzalczany

ISBN 10: 3790814393 / ISBN 13: 9783790814392

Edité par Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg, 2001

A propos de cet article

Hardcover. 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 techniques such as rough sets, decision trees, regression trees, probabilistic rule induction etc. This presentation is preceded by a discussion of the main directions of synthesizing fuzzy sets, artificial neural networks and genetic algorithms in the framework of designing CI systems. In order to keep the book self-contained, introductions to the basic concepts of fuzzy systems, artificial neural networks and genetic algorithms are given. This book is intended for researchers and practitioners in AI/CI fields and for students of computer science or neighbouring areas. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge "buried" in past-experience expert) and numerical data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

N° de réf. du vendeur 9783790814392

Détails bibliographiques

• Titre: Computational Intelligence Systems and Applications (Hardcover)
• Éditeur: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg
• Date de parution: 2001
• Langue: anglais
• ISBN à 10 chiffres: 3790814393
• ISBN à 13 chiffres: 9783790814392
• Reliure: Hardcover
• État de l'article: new

A propos de ce titre

Synopsis

Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real- world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge "buried" in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge "encoded" in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.