We propose a Particle Filter model that incorporates Particle Swarm Optimization for predicting systems with multiplicative noise. The proposed model employs a conventional multiobjective optimization approach to weight the likelihood and prior of the filter in order to alleviate the particle impoverishment problem. The resulting scheme is tested on a well-known test problem with multiplicative noise. Results are promising, especially in cases of high system and measurement noise levels. Copyright 2008 ACM.
CITATION STYLE
Klamargias, A. D., Parsopoulos, K. E., Vrahatis, M. N., & Alevizos, P. D. (2008). Particle filtering with particle swarm optimization in systems with multiplicative noise. In GECCO’08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 (pp. 57–62). Association for Computing Machinery (ACM). https://doi.org/10.1145/1389095.1389104
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