Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms (Studies in Fuzziness and Soft Computing, 86)

Gorzalczany, Marian B.

ISBN 10: 3790814393 / ISBN 13: 9783790814392

Edité par Physica, 2001

A propos de cet article

N° de réf. du vendeur I-9783790814392

Détails bibliographiques

• Titre: Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms (Studies in Fuzziness and Soft Computing, 86)
• Éditeur: Physica
• Date de parution: 2001
• Langue: anglais
• ISBN à 10 chiffres: 3790814393
• ISBN à 13 chiffres: 9783790814392
• Reliure: Couverture rigide
• 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.