Journal
ADVANCES IN ENGINEERING SOFTWARE
Volume 40, Issue 10, Pages 1087-1094Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.advengsoft.2009.01.029
Keywords
Hybrid GMDH-type algorithm; Neural networks; SOPNN; Heuristic approximation; System identification
Categories
Funding
- Korean Government (MOEHRD) [KRF-2007357-1-100006, KRF-2008-521-D00137]
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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. (C) 2009 Elsevier Ltd. All rights reserved.
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