| Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms 2002 Edition Contributor(s): Gorzalczany, Marian B. (Author) |
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ISBN: 3790814393 ISBN-13: 9783790814392 Publisher: Physica-Verlag OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: December 2001 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Medical - Computers | Intelligence (ai) & Semantics - Science | System Theory |
| Dewey: 003.7 |
| LCCN: 2002283062 |
| Series: Studies in Fuzziness and Soft Computing |
| Physical Information: 0.88" H x 6.14" W x 9.21" (1.56 lbs) 364 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |