Artificial Neural Networks in Hydrology:
R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
2000
|
| Schriftenreihe: | Water Science and Technology Library
36 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy |
| Beschreibung: | 1 Online-Ressource (XVI, 332 p) |
| ISBN: | 9789401593410 |
| DOI: | 10.1007/978-94-015-9341-0 |
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| 520 | |a R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy | ||
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| spelling | Artificial Neural Networks in Hydrology edited by R. S. Govindaraju, A. Ramachandra Rao Dordrecht Springer Netherlands 2000 1 Online-Ressource (XVI, 332 p) txt rdacontent c rdamedia cr rdacarrier Water Science and Technology Library 36 R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy Earth Sciences Hydrogeology Statistical Physics, Dynamical Systems and Complexity Theory of Computation Earth sciences Computers Statistical physics Dynamical systems Govindaraju, R. S. edt Rao, A. Ramachandra edt Erscheint auch als Druck-Ausgabe 9789048154210 https://doi.org/10.1007/978-94-015-9341-0 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Artificial Neural Networks in Hydrology Earth Sciences Hydrogeology Statistical Physics, Dynamical Systems and Complexity Theory of Computation Earth sciences Computers Statistical physics Dynamical systems |
| title | Artificial Neural Networks in Hydrology |
| title_auth | Artificial Neural Networks in Hydrology |
| title_exact_search | Artificial Neural Networks in Hydrology |
| title_full | Artificial Neural Networks in Hydrology edited by R. S. Govindaraju, A. Ramachandra Rao |
| title_fullStr | Artificial Neural Networks in Hydrology edited by R. S. Govindaraju, A. Ramachandra Rao |
| title_full_unstemmed | Artificial Neural Networks in Hydrology edited by R. S. Govindaraju, A. Ramachandra Rao |
| title_short | Artificial Neural Networks in Hydrology |
| title_sort | artificial neural networks in hydrology |
| topic | Earth Sciences Hydrogeology Statistical Physics, Dynamical Systems and Complexity Theory of Computation Earth sciences Computers Statistical physics Dynamical systems |
| topic_facet | Earth Sciences Hydrogeology Statistical Physics, Dynamical Systems and Complexity Theory of Computation Earth sciences Computers Statistical physics Dynamical systems |
| url | https://doi.org/10.1007/978-94-015-9341-0 |
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