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Erschienen in:
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2013 | OriginalPaper | Buchkapitel

2. Introduction to Mapping and Neural Networks

verfasst von : Vladimir M. Krasnopolsky

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

Verlag: 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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Vorheriges Kapitel Introduction
Nächstes Kapitel Atmospheric and Oceanic Remote Sensing Applications
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Metadaten
Titel
Introduction to Mapping and Neural Networks
verfasst von
Vladimir M. Krasnopolsky
Copyright-Jahr
2013
Verlag
Springer Netherlands
Buch
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-Jahr: 2013

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

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