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• W4293722677 endingPage "114794" @default.
• W4293722677 startingPage "114794" @default.
• W4293722677 abstract "This paper considers the combined parameter and state estimation problem of a bilinear state space system with moving average noise. There are product terms of state variables and control variables in bilinear systems, which brings difficulties to parameter and state estimation. By designing a bilinear state estimator based on Kalman filter and using input–output data to estimate the state, a hierarchical multi-innovation stochastic gradient (i.e., H-MISG) algorithm based on the state estimator is proposed to jointly estimate unknown states and parameters. In addition, compared with the hierarchical stochastic gradient algorithm, H-MISG algorithm introduces the innovation length parameter, makes full use of the system input and output data information, and improves the accuracy of parameter estimation. Numerical simulation examples verify the effectiveness of the proposed algorithm." @default.
• W4293722677 created "2022-08-31" @default.
• W4293722677 creator A5019565287 @default.
• W4293722677 creator A5034959305 @default.
• W4293722677 creator A5041735214 @default.
• W4293722677 creator A5052283123 @default.
• W4293722677 date "2023-03-01" @default.
• W4293722677 modified "2023-10-17" @default.
• W4293722677 title "Hierarchical multi-innovation stochastic gradient identification algorithm for estimating a bilinear state-space model with moving average noise" @default.
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• W4293722677 doi "https://doi.org/10.1016/j.cam.2022.114794" @default.
• W4293722677 hasPublicationYear "2023" @default.
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