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Particle filter (PF) is an effective approach for the state estimation of nonlinear systems. However, PF still faces many problems which influence the accuracy of state estimation. One typical problem is the particle impoverishment. In this study, an adaptive intelligent particle filter (AIPF) is proposed to mitigate the particle impoverishment problem common in PF. We adopt the idea of evolution...
The particle filter (PF) is an effective technique for state estimation in the nonlinear and non-Gaussian systems. However, one serious problem of PF is resampling. In this study, a weights PF (WPF) is proposed to mitigate the particle impoverishment problem common in PF. The WPF is inspired by the selection operator of genetic algorithm (GA). The weights of WPF are divided into two parts, and the...
With the continuous development of computer science and control science, the complexity of the system also increased rapidly. Accordingly, people began to improve the security and stability of the systems, and fault diagnosis in time is an effectively method to reduce the loss of property. The reality systems are invariably more complex, nonlinear and non-Gaussian. The previous method cannot solve...
Particle filter (PF) serves as an effective method applied to the fault diagnosis of nonlinear and non-Gaussian systems. However, the result of state estimation is influenced by the particle impoverishment problem which is common in the typical PF algorithm. Based on the analysis of the PF algorithm, the general particle impoverishment problem is attributed to the deficiency of particle diversity...
Practical production systems are usually complex, nonlinear and non-Gaussian. Different from some other fault diagnosis methods, particle filter can applied to nonlinear and non-Gaussian systems effectively. The particle impoverishment problem exists in the traditional particle filter algorithm, which influences the results of state estimation. In this paper, we conclude that the general particle...
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