Hybrid GMDH-type modeling for nonlinear systems: Synergism to intelligent identification |
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Authors: | Dongwon Kim Sam-Jun Seo Gwi-Tae Park |
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Affiliation: | 1. Department of Civil Engineering, Razi University, Kermanshah, Iran;2. Water Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran.;3. Department of Computer System, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;1. Boehlerit GmbH & Co KG, Austria;2. Shu Powders Ltd, South Africa |
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Abstract: | This paper presents a novel hybrid GMDH-type algorithm which combines neural networks (NNs) with an approximation scheme (self-organizing polynomial neural network: SOPNN). This composite structure is developed to establish a new heuristic approximation method for identification of nonlinear static systems. NNs have been widely employed to process modeling and control because of their approximation capabilities. And SOPNN is an analysis technique for identifying nonlinear relationships between the inputs and outputs of such systems and builds hierarchical polynomial regressions of required complexity. Therefore, the combined model can harmonize NNs with SOPNN and find a workable synergistic environment. Simulation results of the nonlinear static system are provided to show that the proposed method is much more accurate than other modeling methods. Thus, it can be considered for efficient system identification methodology. |
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