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Zustand: Fair. : Sumérgete en la atmósfera misteriosa y encantadora de octubre con 'Taletober: Breves historias para las noches de octubre', una colección de relatos cortos de Alma Alanís. Este libro te invita a explorar historias concisas y evocadoras, perfectas para leer en las noches frescas de otoño. Cada relato captura la esencia de la temporada, desde lo espeluznante hasta lo mágico, ideal para los amantes de la literatura de ficción y fantasía.Este volumen recoge los microrrelatos nacidos en 2023 al cobijo del Taletober, el reto de creación literaria que propone escribir una breve historia al día durante el mes de octubre. EAN: 9798340742018 Tipo: Libros Categoría: Ciencia Ficción|Fantasía Título: Taletober: Breves historias para las noches de octubre Autor: Alma Alanís Editorial: Independently published Idioma: es-ES Páginas: 90 Formato: tapa blanda.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Neural networks have become a well-established methodology as exempli ed by their applications to identi cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o ers a better suited tool to model and control of nonlinear systems. There exist di erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better tted for real-time implementations.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Neural networks have become a well-established methodology as exempli ed by their applications to identi cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o ers a better suited tool to model and control of nonlinear systems. There exist di erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better tted for real-time implementations.…

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Zustand: New. Nancy Arana-Daniel received her B.Sc. Degree from the University of Guadalajara in 2000, and her M. Sc. And Ph.D. degrees in electric engineering with the special field in computer science from Research Center of the National Polytechnic.

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