| 書目名稱 | Feed-Forward Neural Networks |
| 副標(biāo)題 | Vector Decomposition |
| 編輯 | Anne-Johan Annema |
| 視頻video | http://file.papertrans.cn/342/341608/341608.mp4 |
| 叢書名稱 | The Springer International Series in Engineering and Computer Science |
| 圖書封面 |  |
| 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 |
| 關(guān)鍵詞 | 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 |
|Archiver|手機(jī)版|小黑屋|
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