Distance-based Multilayer Perceptrons.

Wlodzislaw Duch, Rafal Adamczak and Geerd H.F. Diercksen
Department of Informatics,  Nicolaus Copernicus University,
Grudziadzka 5, 87-100 Torun, Poland.
 


Neural network models are presented as special cases of a framework for general Similarity-Based Methods (SBMs). Distance-based multilayer perceptrons (D-MLPs) with non-Euclidean metric functions are described. D-MLPs evaluate similarity to prototypes making the interpretation of the results easier. Renormalization of the input data in the extended feature space brings dramatic changes in the shapes of decision borders. An illustrative example showing these changes is provided.

Reference:Computational Intelligence for Modelling Control and Automation. Neural Networks and Advanced Control Strategies. Ed. M. Mohammadian, IOS Press, Amsterdam, pp. 75-80

Presented at International Conference on Computational Intelligence for Modelling Control and Automation,
17-19 February 1999, Vienna, Austria

Paper in PDF, 701 kB

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