Abstract
This paper proposes kalman particle swarm optimization algorithm (KPSO), which combines kalman filter with PSO. Comparison of optimization performance between KPSO and PSO with three test functions shows that KPSO has better optimization performance than PSO. The combination of KPSO and ANN is also introduced (KPSONN). Then, KPSONN is applied to construct a soft-sensor of acrylonitrile yield. After comparing with practical industrial data, the result shows that KPSONN is feasible and effective in soft-sensor of acrylonitrile yield.
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© 2006 Springer-Verlag Berlin Heidelberg
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Xu, Y., Chen, G., Yu, J. (2006). The Kalman Particle Swarm Optimization Algorithm and Its Application in Soft-Sensor of Acrylonitrile Yield. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_22
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DOI: https://doi.org/10.1007/11881223_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
Online ISBN: 978-3-540-45909-5
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