Dynamic GMDH Type Neural Networks
This paper presents a new identification method based on Artificial Neural Networks (ANNs) which can be used for both static and dynamic systems. In particular, a Group Method of Data Handling (GMDH) type neural network with dynamic neurons is considered. The final part of this work contains an illustrative example regarding an application of the proposed approach to the real system identification task.
KeywordsQuality Index Infinite Impulse Response Linear Dynamic System Filter Module Dynamic Neuron
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- 1.Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R. (2000) Biocybernetics and Biomedical Engineering 2000. Neural Networks. Academic Publishing House, Exit, Warsaw (in Polish)Google Scholar
- 2.Edelmayer A. (2000) Research of quantitative and qualitative FDI methods based on data from Lublin Sugar Factory. Proc. IFAC Symp. SAFEPROCESS’2000. Budapest, Hungary, 331–358Google Scholar
- 3.Farlow S. J. (1995) Self-organizing Methods in Modelling. GMDH-type Algorithms. Marcel Dekker Inc., New YorkGoogle Scholar
- 4.Ivakhnenko A. G., Muller J. A. (1995) Self-organizing of nets of active neurons. System Analysis Modelling Simulation. 20, 93–106Google Scholar
- 6.Mrugalski M., Witczak M. (2002) Parameter estimation of dynamic GMDH neural networks with the bounded-error technique. Jour. Appl. Comp. Sci. (in print).Google Scholar
- 8.Pham D. T., Xing L. (1995) Neural Networks for Identification, Prediction and Control. Springer Verlag, LondonGoogle Scholar