Abstract
A modelling process of an unknown multi-dimensional system is mostly performed with methods which describe the system by a multi-dimensional surface (e.g. neural networks (NNs)). Some systems, however, does not have a surface nature. On the contrary – their behavior resembles multi-dimensional chains. Obviously, as it was proven in numerous applications, always better results can be obtained when the modelling method corresponds to the system nature. Therefore, when a data distribution of an unknown system has a chain characteristic, the system should be also modelled with a chain, not a surface, method. The aim of this article is to present the alternative approach to the modelling process, in which the multi-dimensional model of an unknown system is built on the basis of a set of two-dimensional NNs instead of one multi-dimensional NN. The proposed approach results in a chain multi-dimensional model of an analyzed system.
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© 2006 Springer-Verlag Berlin Heidelberg
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Piegat, A., Rejer, I., Mikolajczyk, M. (2006). Application of Neural Networks in Chain Curve Modelling. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_12
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DOI: https://doi.org/10.1007/11785231_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35748-3
Online ISBN: 978-3-540-35750-6
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