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Titlebook: Networks of Learning Automata; Techniques for Onlin M. A. L. Thathachar,P. S. Sastry Book 2004 Kluwer Academic Publishers 2004 Computer Vis

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發(fā)表于 2025-3-21 16:47:17 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Networks of Learning Automata
副標(biāo)題Techniques for Onlin
編輯M. A. L. Thathachar,P. S. Sastry
視頻videohttp://file.papertrans.cn/664/663009/663009.mp4
圖書封面Titlebook: Networks of Learning Automata; Techniques for Onlin M. A. L. Thathachar,P. S. Sastry Book 2004 Kluwer Academic Publishers 2004 Computer Vis
描述.Networks of Learning Automata: Techniques for Online Stochastic Optimization. is a comprehensive account of learning automata models with emphasis on multiautomata systems. It considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining probabilities of selecting actions. Mathematical analysis of the behavior of games and feedforward networks is provided. Algorithms considered here can be used for online optimization of systems based on noisy measurements of performance index. Also, algorithms that assure convergence to the global optimum are presented. Parallel operation of automata systems for improving speed of convergence is described. The authors also include extensive discussion of how learning automata solutions can be constructed in a variety of applications.
出版日期Book 2004
關(guān)鍵詞Computer Vision; Performance; Simulation; Stochastic Approximation; Stochastic Optimization; algorithms; c
版次1
doihttps://doi.org/10.1007/978-1-4419-9052-5
isbn_softcover978-1-4613-4775-0
isbn_ebook978-1-4419-9052-5
copyrightKluwer Academic Publishers 2004
The information of publication is updating

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Games of Learning Automata,known in the sense that only noise corrupted values of the function for any specific values of arguments are observable. Suppose we want to find the maximum of a function .: ? → ? given only noise corrupted observations.
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Introduction,tly different, the two endeavors are nonetheless interrelated, because success in one helps to improve our abilities in the other. Different approaches (with varying levels of relationship to human learning) for incorporating learning ability in machines have been investigated by engineers interested in the general area of machine intelligence.
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