Introduction to Mapping and Neural Networks

  • Vladimir M. Krasnopolsky
Part of the Atmospheric and Oceanographic Sciences Library book series (ATSL, volume 46)


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.


Hide Neuron Ensemble Member Target Mapping Lookup Table Stochastic Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht(outside the USA.) 2013

Authors and Affiliations

  • Vladimir M. Krasnopolsky
    • 1
  1. 1.NOAA Center for Weather and Climate PredictionCamp SpringUSA

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