Function Approximation Using Generalized by Hsu Pei Hsun (6 results)
Language: English
Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
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Taschenbuch. Condition: Neu. Function Approximation Using Generalized Adalines | Fundamental Multi-state Neural Organizations | Pei-Hsun Hsu (u. a.) | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639225723 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]pre…igu[dot]de | Anbieter: preigu.
Language: English
Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
- Softcover
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hsu Pei-HsunPei-Hsun Hsu was born in Taiwan. She received the B.S. degree in mathematics from National Central University, M.S. degree in applied mathematics from National Dong…Hwa University and now is the Ph.D. candidate of compute.
Language: English
Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
- Softcover
- Print on Demand
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Condition: New. PRINT ON DEMAND pp. 64.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Adaline (adaptive linear element) was proposed by Widrow in 1960s and it has been widely applied to construct neural networks in solving tasks of classification, noise cancellation, system identification and signal prediction. An adaline… is composed of a receptive field and a threshold function with bipolar output. In this work, we generalize the bipolar threshold function to multi-state transfer function successfully and prove that adaline and perceptron are special cases of it. The supervised learning process is modeled by a mathematical framework mixed with integer and linear programming and solved by a hybrid of mean field annealing and gradient descent methods according to the criteria of minimizing design cost, maximizing utilization of Gaussian units subject to minimal model size. The numerical simulations show that the learning process is able to generate essential internal representations for the mapping underlying training samples.
