Knowledge-based neurocomputing:
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass.
MIT Press
©2000
|
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references and index Knowledge-based neurocomputing : past, present, and future -- - Architectures and techniques for knowledge-based neurocomputing -- - Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- - Tutorial on neurocomputing of structures -- - Structural learning and rule discovery -- - VL₁ANN : transformation of rules to artificial neural networks -- - Integration of heterogeneous sources of partial domain knowledge -- - Approximation of differential equations using neural networks -- - Fynesse : a hybrid architecture for self-learning control -- - Data mining techniques for designing neural network time series predictors -- - Extraction of decision trees from artificial networks -- - Extraction of linguistic rules from data via neural networks and fuzzy approximation -- - Neural knowledge processing in expert systems Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors : C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada |
| Beschreibung: | 1 Online-Ressource (xiv, 486 pages) |
| ISBN: | 0262032740 0585355010 9780262032742 9780585355016 |
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| 500 | |a Knowledge-based neurocomputing : past, present, and future -- - Architectures and techniques for knowledge-based neurocomputing -- - Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- - Tutorial on neurocomputing of structures -- - Structural learning and rule discovery -- - VL₁ANN : transformation of rules to artificial neural networks -- - Integration of heterogeneous sources of partial domain knowledge -- - Approximation of differential equations using neural networks -- - Fynesse : a hybrid architecture for self-learning control -- - Data mining techniques for designing neural network time series predictors -- - Extraction of decision trees from artificial networks -- - Extraction of linguistic rules from data via neural networks and fuzzy approximation -- - Neural knowledge processing in expert systems | ||
| 500 | |a Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors : C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada | ||
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| 700 | 1 | |a Zurada, Jacek M. |e Sonstige |4 oth | |
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| spelling | Knowledge-based neurocomputing edited by Ian Cloete and Jacek M. Zurada Cambridge, Mass. MIT Press ©2000 1 Online-Ressource (xiv, 486 pages) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index Knowledge-based neurocomputing : past, present, and future -- - Architectures and techniques for knowledge-based neurocomputing -- - Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- - Tutorial on neurocomputing of structures -- - Structural learning and rule discovery -- - VL₁ANN : transformation of rules to artificial neural networks -- - Integration of heterogeneous sources of partial domain knowledge -- - Approximation of differential equations using neural networks -- - Fynesse : a hybrid architecture for self-learning control -- - Data mining techniques for designing neural network time series predictors -- - Extraction of decision trees from artificial networks -- - Extraction of linguistic rules from data via neural networks and fuzzy approximation -- - Neural knowledge processing in expert systems Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors : C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada COMPUTERS / Neural Networks bisacsh Expert systems (Computer science) fast Neural computers fast Neural computers Expert systems (Computer science) Cloete, Ian Sonstige oth Zurada, Jacek M. Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=49735 Aggregator Volltext |
| spellingShingle | Knowledge-based neurocomputing COMPUTERS / Neural Networks bisacsh Expert systems (Computer science) fast Neural computers fast Neural computers Expert systems (Computer science) |
| title | Knowledge-based neurocomputing |
| title_auth | Knowledge-based neurocomputing |
| title_exact_search | Knowledge-based neurocomputing |
| title_full | Knowledge-based neurocomputing edited by Ian Cloete and Jacek M. Zurada |
| title_fullStr | Knowledge-based neurocomputing edited by Ian Cloete and Jacek M. Zurada |
| title_full_unstemmed | Knowledge-based neurocomputing edited by Ian Cloete and Jacek M. Zurada |
| title_short | Knowledge-based neurocomputing |
| title_sort | knowledge based neurocomputing |
| topic | COMPUTERS / Neural Networks bisacsh Expert systems (Computer science) fast Neural computers fast Neural computers Expert systems (Computer science) |
| topic_facet | COMPUTERS / Neural Networks Expert systems (Computer science) Neural computers |
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