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

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...

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Vydané v:Artificial life and robotics Ročník 15; číslo 2; s. 199 - 202
Hlavní autori: Jau, You-Min, Wu, Chia-Ju, Jeng, Jin-Tsong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Japan Springer Japan 01.09.2010
Predmet:
Algorithms
Artificial Intelligence
Computation by Abstract Devices
Computer Science
Computer simulation
Control
Estimates
Exact solutions
Integrals
Mathematical models
Mechatronics
Nonlinearity
Optimization
Original Article
Robotics
Nonlinear multi-regression
Choquet integral
Quantum-behaved particle swarm optimization
Estimation
ISSN:1433-5298, 1614-7456
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Shrnutí: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.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-010-0790-y