Interacting T-S Fuzzy Model Particle Filter

To the uncertainty modeling of nonlinear non-Gaussian dynamic system, a novel interacting T-S fuzzy model particle filter is proposed. In the proposed algorithm, a general interacting T-S fuzzy model framework is constructed. The target feature information is represented by multiple semantic fuzzy s...

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Published in:2019 22th International Conference on Information Fusion (FUSION) pp. 1 - 6
Main Authors: Li, Liang-Qun, Wang, Xiao-Li, Xie, Wei-Xin
Format: Conference Proceeding
Language:English
Published: ISIF - International Society of Information Fusion 01.07.2019
Subjects:
Adaptation models
Clustering algorithms
Fuzzy C-regression clustering
Fuzzy sets
Heuristic algorithms
Particle filter
Particle filters
Semantics
T-S fuzzy model
Uncertainty
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Abstract To the uncertainty modeling of nonlinear non-Gaussian dynamic system, a novel interacting T-S fuzzy model particle filter is proposed. In the proposed algorithm, a general interacting T-S fuzzy model framework is constructed. The target feature information is represented by multiple semantic fuzzy sets, and the probability transition model between fuzzy sets is derived based on the closeness degree between fuzzy sets, which is used to estimate the interacting transition probabilities between the models. Furthermore, to identify the premise parameters of T-S fuzzy model, a fuzzy C- regression clustering method is proposed, and a particle filtering algorithm based on a modified extended forgetting recursive least squares estimation method (EFRLS) is used to identify the consequence parameters. Finally, the experimental results show that the tracking performance of the proposed algorithms is better than that of the traditional interacting multiple model (IMM), interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).
AbstractList To the uncertainty modeling of nonlinear non-Gaussian dynamic system, a novel interacting T-S fuzzy model particle filter is proposed. In the proposed algorithm, a general interacting T-S fuzzy model framework is constructed. The target feature information is represented by multiple semantic fuzzy sets, and the probability transition model between fuzzy sets is derived based on the closeness degree between fuzzy sets, which is used to estimate the interacting transition probabilities between the models. Furthermore, to identify the premise parameters of T-S fuzzy model, a fuzzy C- regression clustering method is proposed, and a particle filtering algorithm based on a modified extended forgetting recursive least squares estimation method (EFRLS) is used to identify the consequence parameters. Finally, the experimental results show that the tracking performance of the proposed algorithms is better than that of the traditional interacting multiple model (IMM), interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).
Author Wang, Xiao-Li
Xie, Wei-Xin
Li, Liang-Qun
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  organization: ATR Key Laboratory, Shenzhen University,Shenzhen,P.R.China,518060
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Snippet To the uncertainty modeling of nonlinear non-Gaussian dynamic system, a novel interacting T-S fuzzy model particle filter is proposed. In the proposed...
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SubjectTerms Adaptation models
Clustering algorithms
Fuzzy C-regression clustering
Fuzzy sets
Heuristic algorithms
Particle filter
Particle filters
Semantics
T-S fuzzy model
Uncertainty
Title Interacting T-S Fuzzy Model Particle Filter
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