| 书目名称 | Feed-Forward Neural Networks | | 副标题 | Vector Decomposition | | 编辑 | Anne-Johan Annema | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | .Feed-Forward Neural Networks: Vector Decomposition Analysis,Modelling. .and Analog Implementation. presents a novel methodfor the mathematical analysis of neural networks that learn accordingto the back-propagation algorithm. The book also discusses some otherrecent alternative algorithms for hardware implemented perception-likeneural networks. The method permits a simple analysis of the learningbehaviour of neural networks, allowing specifications for theirbuilding blocks to be readily obtained. .Starting with the derivation of a specification and ending with itshardware implementation, analog hard-wired, feed-forward neuralnetworks with on-chip back-propagation learning are designed in theirentirety. On-chip learning is necessary in circumstances where fixedweight configurations cannot be used. It is also useful for theelimination of most mis-matches and parameter tolerances that occur inhard-wired neural network chips. .Fully analog neural networks have several advantages over otherimplementations: low chip area, low power consumption, and high speedoperation. ..Feed-Forward Neural Networks. is an excellent source of referenceand may be used as a text for advanced courses. . | | 出版日期 | Book 1995 | | 关键词 | Hardware; Signal; analog; behavior; learning; modeling; network; neural networks; perception | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-2337-6 | | isbn_softcover | 978-1-4613-5990-6 | | isbn_ebook | 978-1-4615-2337-6Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 1995 |
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发表于 2025-3-21 16:56:19
