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Titlebook: Cellular Automata, Dynamical Systems and Neural Networks; Eric Goles,Servet Martínez Book 1994 Springer Science+Business Media Dordrecht 1

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書目名稱Cellular Automata, Dynamical Systems and Neural Networks
編輯Eric Goles,Servet Martínez
視頻videohttp://file.papertrans.cn/223/222998/222998.mp4
叢書名稱Mathematics and Its Applications
圖書封面Titlebook: Cellular Automata, Dynamical Systems and Neural Networks;  Eric Goles,Servet Martínez Book 1994 Springer Science+Business Media Dordrecht 1
描述This book contains the courses given at the Third School on Statistical Physics and Cooperative Systems held at Santiago, Chile, from 14th to 18th December 1992. The main idea of this periodic school was to bring together scientists work- with recent trends in Statistical Physics. More precisely ing on subjects related related with non linear phenomena, dynamical systems, ergodic theory, cellular au- tomata, symbolic dynamics, large deviation theory and neural networks. Scientists working in these subjects come from several areas: mathematics, biology, physics, computer science, electrical engineering and artificial intelligence. Recently, a very important cross-fertilization has taken place with regard to the aforesaid scientific and technological disciplines, so as to give a new approach to the research whose common core remains in statistical physics. Each contribution is devoted to one or more of the previous subjects. In most cases they are structured as surveys, presenting at the same time an original point of view about the topic and showing mostly new results. The expository text of Fran
出版日期Book 1994
關(guān)鍵詞automata; entropy; network; neural networks; statistical physics; theoretical computer science
版次1
doihttps://doi.org/10.1007/978-94-017-1005-3
isbn_softcover978-90-481-4382-5
isbn_ebook978-94-017-1005-3
copyrightSpringer Science+Business Media Dordrecht 1994
The information of publication is updating

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https://doi.org/10.1007/978-3-322-86731-5a fluid, the Heisenberberg model of ferromagnetism, the mass and spring model of lattice vibrations, the Landau model of phase transitions, the Ising model of cooperative phenomena, to mention just a few, have played a major role. A simple model, if it captures the key elements of a complex system, may elicit highly relevant questions.
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Cellular Automata and Transducers. A Topological View,topological, notions from Dynamical Systems play an important part in both, and finally because one of the underlying aims is to study the transformations of the interval associated to some of these automata.
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Storage of Correlated Patterns in Neural Networks,ets is described in more detail. We can store patterns in the sub-nets, by varying the interaction between these, we obtain an efficient way to store correlated patterns that can be related to the human ability to memorize and retrieve words.
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Ein vereinfachtes Planungskonzeption of normality by transducers; here we give a detailed account of the main proof in [2], that of normality preservation under multiplication by rationals. The second instance is an introduction to the dynamical properties of onto cellular automata—since there are but a few known results about them
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https://doi.org/10.1007/978-3-322-86731-5 system many features are likely to be important. Not all of them, however, should be included in the model. Only the few relevant features which are thought to play an essential role in the interpretation of the observed phenomena should be retained. Such simplified descriptions should not be criti
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