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Title: New GOPSO and its application to robust identification
Authors: Baghel V.
Nanda S.J.
Panda G.
Keywords: FLANN
Orthogonal PSO
Robust Identification
Wilcoxon Norm
Issue Date: 2011
Citation: 4
Abstract: Modeling of complex nonlinear systems has become a challenging task in presence of outliers. In this scenario a robust norm with an evolutionary approach does a potential job. A modified evolutionary algorithm GOPSO (global selection based orthogonal PSO) is proposed which offers a more accurate and computationally efficient training compared to OPSO (Orthogonal PSO). The potential of the proposed algorithm has been demonstrated on six benchmark multi-modal optimization problems. Further, robust identification models has been developed by combining Wilcoxon norm with a functional link artificial neural network (FLANN) structure trained by the proposed GOPSO. Exhaustive simulation studies on five complex plants show superior performance of proposed models when output of plant gets corrupted upto 50% outliers. � 2011 IEEE.
Appears in Collections:Research Publications

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