The Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings 2013 Edition Contributor(s): Krasnopolsky, Vladimir M. (Author) |
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ISBN: 9400760728 ISBN-13: 9789400760721 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: July 2013 |
Additional Information |
BISAC Categories: - Computers | Intelligence (ai) & Semantics - Science | Earth Sciences - Meteorology & Climatology - Mathematics | Applied |
Dewey: 006.3 |
Series: Atmospheric and Oceanographic Sciences Library |
Physical Information: 0.7" H x 6" W x 9.2" (1.20 lbs) 189 pages |
Descriptions, Reviews, Etc. |
Publisher Description: This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. "This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (...) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (...) will find it an invaluable guide to neural network methods." (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada)
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