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Spatial Analysis and GeoComputation pp 79–102Cite as

Computational Neural Networks — Tools for Spatial Data Analysis

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Abstract

This chapter is a tutorial text that gives an introductory exposure to computational neural networks for students and professional researchers in spatial data analysis. The text covers a wide range of topics important for developing neural networks into an advanced spatial analytic tool for non-parametric modelling. The topics covered include a definition of computational neural networks in mathematical terms and a careful and detailed description of neural networks in terms of the properties of the processing elements, the network topology and learning in the network. The chapter presents four important families of neural networks that are especially attractive for solving real world spatial analysis problems: backpropagation networks, radial basis function networks, supervised and unsupervised ART models, and self-organising feature map networks.

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(2006). Computational Neural Networks — Tools for Spatial Data Analysis. In: Spatial Analysis and GeoComputation. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-35730-0_6

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