Recursive Least Squares and Multi-innovation Gradient Estimation Algorithms for Bilinear Stochastic Systems

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• 作者：Dandan Meng
• 关键词：Parameter estimation ; Least squares ; Multi ; innovation identification ; Bilinear system
• 刊名：Circuits, Systems, and Signal Processing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：36
• 期：3
• 页码：1052-1065
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Electronics and Microelectronics, Instrumentation;
• 出版者：Springer US
• ISSN：1531-5878
• 卷排序：36

文摘

Bilinear systems are a special class of nonlinear systems. Some systems can be described by using bilinear models. This paper considers the parameter identification problems of bilinear stochastic systems. The difficulty of identification is that the model structure of the bilinear systems includes the products of the states and inputs. To this point, this paper gives the input–output representation of the bilinear systems through eliminating the state variables in the model and derives a least squares algorithm and a multi-innovation stochastic gradient algorithm for identifying the parameters of bilinear systems based on the least squares principle and the multi-innovation identification theory. The simulation results indicate that the proposed algorithms are effective for identifying bilinear systems.