| 书目名称 | Theoretical Advances in Neural Computation and Learning | | 编辑 | Vwani Roychowdhury,Kai-Yeung Siu,Alon Orlitsky | | 视频video | | | 图书封面 |  | | 描述 | For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the | | 出版日期 | Book 1994 | | 关键词 | algorithms; artificial neural network; backpropagation; communication; complexity; electrical engineering | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-2696-4 | | isbn_softcover | 978-1-4613-6160-2 | | isbn_ebook | 978-1-4615-2696-4 | | copyright | Springer Science+Business Media New York 1994 |
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发表于 2025-3-21 19:25:09
