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Titlebook: Neural Networks; An Introduction Berndt Müller,Joachim Reinhardt,Michael T. Strickl Book 1995Latest edition Springer-Verlag Berlin Heidelbe

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書目名稱Neural Networks
副標(biāo)題An Introduction
編輯Berndt Müller,Joachim Reinhardt,Michael T. Strickl
視頻videohttp://file.papertrans.cn/664/663698/663698.mp4
叢書名稱Physics of Neural Networks
圖書封面Titlebook: Neural Networks; An Introduction Berndt Müller,Joachim Reinhardt,Michael T. Strickl Book 1995Latest edition Springer-Verlag Berlin Heidelbe
描述.Neural Networks .presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.
出版日期Book 1995Latest edition
關(guān)鍵詞Nervous System; algorithms; evolutionary algorithm; genetic algorithms; learning; neurons; optimization; st
版次2
doihttps://doi.org/10.1007/978-3-642-57760-4
isbn_softcover978-3-540-60207-1
isbn_ebook978-3-642-57760-4Series ISSN 0939-3145
issn_series 0939-3145
copyrightSpringer-Verlag Berlin Heidelberg 1995
The information of publication is updating

書目名稱Neural Networks影響因子(影響力)




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More Applications of Neural Networkssucceed given a specific task. In particular, this is true when no formal solution of the problem is known and the network is expected to discover the solution completely on its own. After all, neural networks of the low degree of complexity discussed here are surely . more intelligent than human beings.
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Combinatorial Optimizationtion problems. Here a . has to be minimized, which depends on the order of a finite number of objects. The number of arrangements of . objects, and therefore the effort to find the minimum of ., grows exponentially with ..
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Unsupervised Learningrespect: that complete knowledge of the deviation of the output from the desired reaction is required to determine the adjustment even of neurons in hidden layers far separated from the output layer. It is hard to believe that such extended back-coupling mechanisms can operate in complex biological neural networks.
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