Application of Neural Networks in Chain Curve Modelling
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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- 1.Aczel, A.: Complete Business Statistics. Richard D. Irwin, Inc., Sydney, Austalia (1993)Google Scholar
- 3.Klesk, P.: The method of setting suitable extrapolation capabilities for neurofuzzy models of multidimensional systems, PhD Thesis, Technical University of Szczecin (2005)Google Scholar
- 4.Masters, T.: Practical Neural Networks Recipes in C++. Academic Press Inc., London (1993)Google Scholar
- 5.Osowski, S.: Neural networks for information processing, The publishing house of the Technical University of Warsaw, Warsaw (2000)Google Scholar
- 6.Rejer, I.: A method of modeling a multi-dimensional system via artificial intelligence methods on the example of an unemployment in Poland. The publishing house of the Szczecin University, Szczecin (2003)Google Scholar