ISBN: 9780792395676 - feed-forward neural networks: vector decomposition analysis, modelling and analog implementation (the springer international series in engineering and computer science, band 314) (6 Ergebnisse)

Zustand: Fine. *Price HAS BEEN REDUCED by 10% until Monday, Oct 26 (SALE item)* 238 pp., Hardcover, BRAND NEW and in shrink wrap, although there is a remainder mark to bottom edge. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.…

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…

Zustand: New. In English.

Zustand: New. Presents a method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. This book discusses some other alternative algorithms for hardware implemented perception-like neural networks. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 251 pages, biography. BIC Classification: TJF; UYQ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 235 x 155 x 15. Weight in Grams: 537. . 1995. Hardback. . . . . Books ship from the US and Ireland.…

Gebunden. Zustand: New. Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book a.

Buch. Zustand: Neu. Neuware - Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.…