Cellular Neural Networks: Dynamics and Modelling
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
2003
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| Schriftenreihe: | Mathematical Modelling: Theory and Applications
16 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
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| 100 | 1 | |a Slavova, Angela |e Verfasser |4 aut | |
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| 500 | |a Conventional digital computation methods have run into a serious speed bottleneck due to their serial nature. To overcome this problem, a new computation model, called Neural Networks, has been proposed, which is based on some aspects of neurobiology and adapted to integrated circuits. The increased availability of computing power has not only made many new applications possible but has also created the desire to perform cognitive tasks which are easily carried out by the human brain. It become obvious that new types of algorithms and/or circuits were necessary to cope with such tasks. Inspiration has been sought from the functioning of the human brain, which led to the artificial neural network approach. One way of looking at neural networks is to consider them to be arrays of nonlinear dynamical systems that interact with each other. This book deals with one class of locally coupled neural networks, called Cellular Neural Networks (CNNs). CNNs were introduced in 1988 by L. O. Chua and L. Yang [27,28] as a novel class of information processing systems, which posseses some of the key features of neural networks (NNs) and which has important potential applications in such areas as image processing and pattern recognition. Unfortunately, the highly interdisciplinary nature of the research in CNNs makes it very difficult for a newcomer to enter this important and fasciriating area of modern science | ||
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Datensatz im Suchindex
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| any_adam_object | |
| author | Slavova, Angela |
| author_facet | Slavova, Angela |
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| dewey-full | 621 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621 |
| dewey-search | 621 |
| dewey-sort | 3621 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-94-017-0261-4 |
| format | Electronic eBook |
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| id | DE-604.BV042424195 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-24T04:23:29Z |
| institution | BVB |
| isbn | 9789401702614 9789048162543 |
| issn | 1386-2960 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027859612 |
| oclc_num | 864074146 |
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| physical | 1 Online-Ressource (X, 220 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 2003 |
| publishDateSearch | 2003 |
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| publisher | Springer Netherlands |
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| series2 | Mathematical Modelling: Theory and Applications |
| spelling | Slavova, Angela Verfasser aut Cellular Neural Networks: Dynamics and Modelling by Angela Slavova Dordrecht Springer Netherlands 2003 1 Online-Ressource (X, 220 p) txt rdacontent c rdamedia cr rdacarrier Mathematical Modelling: Theory and Applications 16 1386-2960 Conventional digital computation methods have run into a serious speed bottleneck due to their serial nature. To overcome this problem, a new computation model, called Neural Networks, has been proposed, which is based on some aspects of neurobiology and adapted to integrated circuits. The increased availability of computing power has not only made many new applications possible but has also created the desire to perform cognitive tasks which are easily carried out by the human brain. It become obvious that new types of algorithms and/or circuits were necessary to cope with such tasks. Inspiration has been sought from the functioning of the human brain, which led to the artificial neural network approach. One way of looking at neural networks is to consider them to be arrays of nonlinear dynamical systems that interact with each other. This book deals with one class of locally coupled neural networks, called Cellular Neural Networks (CNNs). CNNs were introduced in 1988 by L. O. Chua and L. Yang [27,28] as a novel class of information processing systems, which posseses some of the key features of neural networks (NNs) and which has important potential applications in such areas as image processing and pattern recognition. Unfortunately, the highly interdisciplinary nature of the research in CNNs makes it very difficult for a newcomer to enter this important and fasciriating area of modern science Physics Neurosciences Differential Equations Differential equations, partial Statistical Physics, Dynamical Systems and Complexity Mathematical Modeling and Industrial Mathematics Ordinary Differential Equations Partial Differential Equations Mathematical Modelling Theory and Applications 16 (DE-604)BV011613239 16 https://doi.org/10.1007/978-94-017-0261-4 Verlag Volltext |
| spellingShingle | Slavova, Angela Cellular Neural Networks: Dynamics and Modelling Physics Neurosciences Differential Equations Differential equations, partial Statistical Physics, Dynamical Systems and Complexity Mathematical Modeling and Industrial Mathematics Ordinary Differential Equations Partial Differential Equations |
| title | Cellular Neural Networks: Dynamics and Modelling |
| title_auth | Cellular Neural Networks: Dynamics and Modelling |
| title_exact_search | Cellular Neural Networks: Dynamics and Modelling |
| title_full | Cellular Neural Networks: Dynamics and Modelling by Angela Slavova |
| title_fullStr | Cellular Neural Networks: Dynamics and Modelling by Angela Slavova |
| title_full_unstemmed | Cellular Neural Networks: Dynamics and Modelling by Angela Slavova |
| title_short | Cellular Neural Networks: Dynamics and Modelling |
| title_sort | cellular neural networks dynamics and modelling |
| topic | Physics Neurosciences Differential Equations Differential equations, partial Statistical Physics, Dynamical Systems and Complexity Mathematical Modeling and Industrial Mathematics Ordinary Differential Equations Partial Differential Equations |
| topic_facet | Physics Neurosciences Differential Equations Differential equations, partial Statistical Physics, Dynamical Systems and Complexity Mathematical Modeling and Industrial Mathematics Ordinary Differential Equations Partial Differential Equations |
| url | https://doi.org/10.1007/978-94-017-0261-4 |
| volume_link | (DE-604)BV011613239 |
| work_keys_str_mv | AT slavovaangela cellularneuralnetworksdynamicsandmodelling |