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Titlebook: Industrial Applications of Neural Networks; Project ANNIE Handbo Ian F. Croall,John P. Mason Book 1992 ECSC — EEC — EAEC, Brussels — Luxemb

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發(fā)表于 2025-3-21 18:16:20 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Industrial Applications of Neural Networks
副標題Project ANNIE Handbo
編輯Ian F. Croall,John P. Mason
視頻videohttp://file.papertrans.cn/464/463946/463946.mp4
叢書名稱Research Reports Esprit
圖書封面Titlebook: Industrial Applications of Neural Networks; Project ANNIE Handbo Ian F. Croall,John P. Mason Book 1992 ECSC — EEC — EAEC, Brussels — Luxemb
描述Neural network technology encompasses a class of methodswhich attempt to mimic the basic structures used in thebrain for information processing. Thetechnology is aimed atproblems such as pattern recognition which are difficult fortraditional computational methods. Neural networkshavepotential applications in many industrial areas such asadvanced robotics, operations research, and processengineering.This book isconcerned with the application of neuralnetwork technology to real industrial problems. Itsummarizes a three-year collaborative international projectcalled ANNIE (Applications of Neural Networks for Industryin Europe) which was jointly funded by industry and theEuropean Commissionwithin the ESPRIT programme. As a recordof a working project, the book gives an insight into thereal problems faced in taking a new technologyfrom theworkbench into a live industrial application, and showsjusthow it can be achieved. It stresses the comparison betweenneuralnetworks and conventional approaches. Even thenon-specialist reader will benefit from understanding thelimitations as well as the advantages of the new technology.
出版日期Book 1992
關鍵詞algorithms; artificial intelligence; cognition; control; heuristics; learning; linear optimization; neural
版次1
doihttps://doi.org/10.1007/978-3-642-84837-7
isbn_softcover978-3-540-55875-0
isbn_ebook978-3-642-84837-7
copyrightECSC — EEC — EAEC, Brussels — Luxembourg 1992
The information of publication is updating

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Methodology,f reasons: the detailed discussions derived from 3 years research and many different applications would be far too long; and, more significantly, we feel that any such conclusions would date rather rapidly as new techniques arise in the literature.
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Pattern Recognition,tion and clustering problems, where it is possible to set clear criteria for success, enabling ready comparison of neural network and conventional methods. These two problems are described below, in terms of supervised and unsupervised learning, and their application to a number of real problems will be described more fully later.
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Implementations of Neural Networks,This chapter presents an overview of the methods currently in use for the implementation of neural networks. The simulators developed within ANNIE are also covered here. Possible hardware (VLSI) simulators are also discussed.
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