Abstract
First, based on the particle swarm optimization, an extended particle swarm optimizer with acceleration coefficients (EPSO_AAC) is presented. The personal best particle is replaced by the average of personal best particles in swarm at generation, and time-varying acceleration coefficients are applied by establishing a nonlinear functional relationship between acceleration coefficients and the different of the average fitness of all particles and the fitness of the global best particle. The proposed algorithm uses more particles’ information, and adjusts adaptively “cognition” component and “social” component by time-varying acceleration coefficients, thus improves convergence performance. Then, the proposed algorithm is applied to nonlinear blind source separation. The demixing system of the nonlinear mixtures is modeled using a multi-input multi-output B-spline neural network whose weights are optimized under the criterion of independence of its outputs by EPSO_AAC. The experiment results demonstrate that the proposed algorithms are effective, and have good convergence performance.
The work is supported by the Post Doctor Science Foundation of P.R.C. (2003034062), the Natural Science Foundation of Guangdong Province, P.R.C. (04300015) , the Program for the Development of Science & Technology of Guangzhou, P.R.C.(2004J1-C0323) and the Program for the Development of Science & Technology of Guangzhou Colleges and Universities, P.R.C.(2055).
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Gao, Y., Li, Z., Zheng, H., Liu, H. (2006). Extended Particle Swarm Optimiser with Adaptive Acceleration Coefficients and Its Application in Nonlinear Blind Source Separation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_128
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DOI: https://doi.org/10.1007/11893257_128
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