Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition
With the large amounts of data collected by scientists and the advent of faster computers, the demand for novel computing methods for analyzing biological and scientific data is growing exponentially. Addressing this need, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Co...
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| Tác giả chính: | |
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
| Được phát hành: |
CRC Press
2009
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| Truy cập trực tuyến: | http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1486 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Tóm tắt: | With the large amounts of data collected by scientists and the advent of faster computers, the demand for novel computing methods for analyzing biological and scientific data is growing exponentially. Addressing this need, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks.
Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by an extensive coverage on linear networks as well as multi-layer networks for nonlinear prediction and clustering with a step-by-step explanation of data processing in these networks and all stages of model development illustrated through practical examples and case studies. This is followed by a detailed treatment of data exploration and preprocessing including dimensionality reduction and input selection, model uncertainty assessment, and sensitivity analysis on inputs, errors and model parameters. Later chapters present an extensive coverage on Self Organizing Maps for data clustering, recurrent networks for time series forecasting and other network types suitable for scientific data analysis. Relevant statistical methods are presented through out the book to demonstrate the complementarities of the two approaches while highlighting the superior nonlinear modeling capabilities of neural networks.
In an easy-to-understand format suitable for applied scientists and engineers, this book fills the gap in the market for neural networks tailored to scientific data analysis highlighting all stages of model development. With a multidisciplinary scientific context,
it addresses how neural networks perform linear and nonlinear data analysis, including prediction, classification, clustering, and forecasting. The book makes it possible to apply neural networks to solve difficult practical problems in a variety of fields. |
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