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1. |
Record Nr. |
UNISA990003254210203316 |
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Autore |
CHAMBERS, Ll. G. |
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Titolo |
Integral equations: a short course / Ll. G. Chambers ; consulting editor: professor A. Jeffrey |
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Pubbl/distr/stampa |
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London : International textbook, copyr. 1976 |
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ISBN |
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Descrizione fisica |
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Disciplina |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910483789103321 |
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Autore |
Yalçın Müştak E |
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Titolo |
Reconfigurable Cellular Neural Networks and Their Applications / / by Müştak E. Yalçın, Tuba Ayhan, Ramazan Yeniçeri |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (79 pages) |
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Collana |
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SpringerBriefs in Nonlinear Circuits, , 2520-1433 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Electronic circuits |
Computational Intelligence |
Artificial Intelligence |
Electronic Circuits and Devices |
Circuits and Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Artificial Neural Network Models -- Artificial Olfaction System -- Implementations of CNNs -- Index. |
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Sommario/riassunto |
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This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson–Cowan model. In turn, two properties that are vital in nature are added to the CNN to help it more accurately deliver mimetic behavior: randomness of connection, and the presence of different dynamics (excitatory and inhibitory) within the same network. It uses an ID matrix to determine the location of excitatory and inhibitory neurons, and to reconfigure the network to optimize its topology. The book demonstrates that reconfiguring a single-layer CNN is an easier and more flexible solution than the procedure required in a multilayer CNN, in which excitatory and inhibitory neurons are separate, and that the key CNN criteria of a spatially invariant template and local coupling are fulfilled. In closing, the application of the authors’ neuron population model as a feature extractor is exemplified using odor and electroencephalogram classification. |
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