Competitively Inhibited Neural Networks for Adaptive Parameter Estimation:
Artificial Neural Networks have captured the interest of many researchers in the last five years. As with many young fields, neural network research has been largely empirical in nature, relyingstrongly on simulationstudies ofvarious network models. Empiricism is, of course, essential to any science...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1991
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
111 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | Artificial Neural Networks have captured the interest of many researchers in the last five years. As with many young fields, neural network research has been largely empirical in nature, relyingstrongly on simulationstudies ofvarious network models. Empiricism is, of course, essential to any science for it provides a body of observations allowing initial characterization of the field. Eventually, however, any maturing field must begin the process of validating empirically derived conjectures with rigorous mathematical models. It is in this way that science has always pro ceeded. It is in this way that science provides conclusions that can be used across a variety of applications. This monograph by Michael Lemmon provides just such a theoretical exploration of the role ofcompetition in Artificial Neural Networks. There is "good news" and "bad news" associated with theoretical research in neural networks. The bad news isthat such work usually requires the understanding of and bringing together of results from many seemingly disparate disciplines such as neurobiology, cognitive psychology, theory of differential equations, largc scale systems theory, computer science, and electrical engineering. The good news is that for those capable of making this synthesis, the rewards are rich as exemplified in this monograph |
| Beschreibung: | 1 Online-Ressource (XIII, 142 p) |
| ISBN: | 9781461540441 |
| DOI: | 10.1007/978-1-4615-4044-1 |
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| 520 | |a Artificial Neural Networks have captured the interest of many researchers in the last five years. As with many young fields, neural network research has been largely empirical in nature, relyingstrongly on simulationstudies ofvarious network models. Empiricism is, of course, essential to any science for it provides a body of observations allowing initial characterization of the field. Eventually, however, any maturing field must begin the process of validating empirically derived conjectures with rigorous mathematical models. It is in this way that science has always pro ceeded. It is in this way that science provides conclusions that can be used across a variety of applications. This monograph by Michael Lemmon provides just such a theoretical exploration of the role ofcompetition in Artificial Neural Networks. There is "good news" and "bad news" associated with theoretical research in neural networks. The bad news isthat such work usually requires the understanding of and bringing together of results from many seemingly disparate disciplines such as neurobiology, cognitive psychology, theory of differential equations, largc scale systems theory, computer science, and electrical engineering. The good news is that for those capable of making this synthesis, the rewards are rich as exemplified in this monograph | ||
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Datensatz im Suchindex
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4615-4044-1 |
| format | Electronic eBook |
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| id | DE-604.BV045186536 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:57Z |
| institution | BVB |
| isbn | 9781461540441 |
| language | English |
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| series2 | The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems |
| spelling | Lemmon, Michael Verfasser aut Competitively Inhibited Neural Networks for Adaptive Parameter Estimation by Michael Lemmon Boston, MA Springer US 1991 1 Online-Ressource (XIII, 142 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 111 Artificial Neural Networks have captured the interest of many researchers in the last five years. As with many young fields, neural network research has been largely empirical in nature, relyingstrongly on simulationstudies ofvarious network models. Empiricism is, of course, essential to any science for it provides a body of observations allowing initial characterization of the field. Eventually, however, any maturing field must begin the process of validating empirically derived conjectures with rigorous mathematical models. It is in this way that science has always pro ceeded. It is in this way that science provides conclusions that can be used across a variety of applications. This monograph by Michael Lemmon provides just such a theoretical exploration of the role ofcompetition in Artificial Neural Networks. There is "good news" and "bad news" associated with theoretical research in neural networks. The bad news isthat such work usually requires the understanding of and bringing together of results from many seemingly disparate disciplines such as neurobiology, cognitive psychology, theory of differential equations, largc scale systems theory, computer science, and electrical engineering. The good news is that for those capable of making this synthesis, the rewards are rich as exemplified in this monograph Computer Science Artificial Intelligence (incl. Robotics) Theory of Computation Computer science Computers Artificial intelligence Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Konkurrierende Wechselwirkung (DE-588)4204509-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Konkurrierende Wechselwirkung (DE-588)4204509-5 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9781461368090 https://doi.org/10.1007/978-1-4615-4044-1 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Lemmon, Michael Competitively Inhibited Neural Networks for Adaptive Parameter Estimation Computer Science Artificial Intelligence (incl. Robotics) Theory of Computation Computer science Computers Artificial intelligence Neuronales Netz (DE-588)4226127-2 gnd Konkurrierende Wechselwirkung (DE-588)4204509-5 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4204509-5 |
| title | Competitively Inhibited Neural Networks for Adaptive Parameter Estimation |
| title_auth | Competitively Inhibited Neural Networks for Adaptive Parameter Estimation |
| title_exact_search | Competitively Inhibited Neural Networks for Adaptive Parameter Estimation |
| title_full | Competitively Inhibited Neural Networks for Adaptive Parameter Estimation by Michael Lemmon |
| title_fullStr | Competitively Inhibited Neural Networks for Adaptive Parameter Estimation by Michael Lemmon |
| title_full_unstemmed | Competitively Inhibited Neural Networks for Adaptive Parameter Estimation by Michael Lemmon |
| title_short | Competitively Inhibited Neural Networks for Adaptive Parameter Estimation |
| title_sort | competitively inhibited neural networks for adaptive parameter estimation |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Theory of Computation Computer science Computers Artificial intelligence Neuronales Netz (DE-588)4226127-2 gnd Konkurrierende Wechselwirkung (DE-588)4204509-5 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Theory of Computation Computer science Computers Artificial intelligence Neuronales Netz Konkurrierende Wechselwirkung |
| url | https://doi.org/10.1007/978-1-4615-4044-1 |
| work_keys_str_mv | AT lemmonmichael competitivelyinhibitedneuralnetworksforadaptiveparameterestimation |