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2013 | OriginalPaper | Chapter

2. Introduction to Mapping and Neural Networks

Author : Vladimir M. Krasnopolsky

Published in: The Application of Neural Networks in the Earth System Sciences

Publisher: Springer Netherlands

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Abstract

In this chapter, the major properties of mappings and multilayer perceptron (MLP) neural networks (NNs) are formulated and discussed. Several examples of real-life problems (prediction of time series, interpolation of lookup tables, satellite retrievals, and fast emulations of model physics) that can be considered as complex, nonlinear, and multidimensional mappings are introduced. The power and flexibility of the NN emulation technique as well as its limitations are discussed; also, it is shown how various methods can be designed to bypass or reduce some of these limitations. The chapter contains an extensive list of references giving extended background and further detail to the interested reader on each examined topic. It can be used as a textbook and an introductory reading for students and beginning and advanced investigators interested in learning how to apply the NN technique to emulate various complex, nonlinear, and multidimensional mappings in different fields of science.

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Metadata
Title
Introduction to Mapping and Neural Networks
Author
Vladimir M. Krasnopolsky
Copyright Year
2013
Publisher
Springer Netherlands
Book
The Application of Neural Networks in the Earth System Sciences

Print ISBN: 978-94-007-6072-1

Electronic ISBN: 978-94-007-6073-8

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-94-007-6073-8_2

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