| 書目名稱 | Feedforward Neural Network Methodology | | 編輯 | Terrence L. Fine | | 視頻video | http://file.papertrans.cn/342/341649/341649.mp4 | | 叢書名稱 | Information Science and Statistics | | 圖書封面 |  | | 描述 | The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These two factors have cooperated to at last provide systems engineers and statisticians with a working, prac- cal, and successful ability to routinely make accurate complex, nonlinear models of such ill-understood phenomena as physical, economic, social, and information-based time series and signals and of the patterns h- den in high-dimensional data. The models are based closely on the data itself and require only little prior understanding of the stochastic mec- nisms underlying these phenomena. Among these models, the feedforward neural networks, also called multilayer perceptrons, have lent themselves to the design of the widest range of successful forecasters, pattern clas- ?ers, controllers, and sensors. In a number of problems in optical character recognition and medical diagnostics, such systems provide state-of-the-art performance and such performance is also expected in speech recognition applications. The successful application of feedforward neural networks to time series forecasting has been multiply d | | 出版日期 | Textbook 1999 | | 關(guān)鍵詞 | Time series; algorithms; architecture; artificial neural network; classification; computer-aided design ( | | 版次 | 1 | | doi | https://doi.org/10.1007/b97705 | | isbn_softcover | 978-1-4757-7309-5 | | isbn_ebook | 978-0-387-22649-1Series ISSN 1613-9011 Series E-ISSN 2197-4128 | | issn_series | 1613-9011 | | copyright | Springer Science+Business Media New York 1999 |
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