| 书目名称 | Predictive Modular Neural Networks | | 副标题 | Applications to Time | | 编辑 | Vassilios Petridis,Athanasios Kehagias | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the us | | 出版日期 | Book 1998 | | 关键词 | algorithms; artificial intelligence; classification; computer; control engineering; convergence; data mini | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-5555-1 | | isbn_softcover | 978-1-4613-7540-1 | | isbn_ebook | 978-1-4615-5555-1Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 1998 |
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发表于 2025-3-21 19:41:53
