9783030178390 - Reconfigurable Cellular Neural Networks and Their Applications (SpringerBriefs in Applied Sciences and Technology) de Yalç?n, Mü?tak E. (9 resultados)

Condición: New. pp. 80.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Condición: Neu. Reconfigurable Cellular Neural Networks and Their Applications | Mü¿tak E. Yalç¿n (u. a.) | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | vi | Englisch | 2019 | Springer | EAN 9783030178390 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condición: new. Questo è un articolo print on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 80 pp. Englisch.

Condición: New. Print on Demand pp. 80.

Condición: New. PRINT ON DEMAND pp. 80.

Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Shows readers how neural networks can be adapted for practical applications&nbsp Introduces new research directions based on recent findings using mixed excitatory and inhibitory neuronal populations&nbsp Helps readers apply neural netw.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 80 pp. Englisch.