A fast parameter estimation for nonlinear multi-regressions based on the Choquet integral with quantum-behaved particle swarm optimization

Authors:
You-Min Jau;Chia-Ju Wu;Jin-Tsong Jeng
Affiliations:
Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan;Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan;Department of Computer Science and Information Engineering, National Formosa University, Huwei, Yunlin, Taiwan 632
Venue:
Artificial Life and Robotics
Year:
2010

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Abstract

In general, the inherent interaction among attributes must be considered circumspectly in the study of data mining and information fusion. A nonlinear model with a nonlinear multi-regression model based on the Choquet integral (NMRCI) is suitable for dealing with these problems. However, this NMRCI is an over-determined system and it is difficult to find the analytic solution. Hence, many researchers have proposed many algorithms: namely, the genetic algorithm, the neural network, particle swarm optimization, quantum-behaved particle swarm optimization (QPSO), etc., to estimate the parameters of NMRCI. In this study, a modified QPSO (MQPSO) algorithm, which is used to estimate the parameters of NMRCI, is proposed. That is, the proposed MQPSO algorithm applies the concept of the GA to the QPSO algorithm so that it can improve the convergent speed and conquer the phenomenon of premature. From the simulation results, the proposed MQPSO gives a more precise estimation and faster convergent speed for the estimated parameters of NMRCI.