Data filtering based maximum likelihood extended gradient method for multivariable systems with autoregressive moving average noise |
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| Authors: | Feiyan Chen Feng Ding Ling Xu Tasawar Hayat |
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| Institution: | 1. Department of Mathematical Sciences, Xi’an Jiaotong Liverpool University, Suzhou 215123, PR China;2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;3. Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;4. Department of Mathematics, Quaid-I-Azam University, Islamabad 44000, Pakistan |
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| Abstract: | For multivariable systems with autoregressive moving average noises, we decompose the multivariable system into m subsystems (m denotes the number of outputs) and present a maximum likelihood generalized extended gradient algorithm and a data filtering based maximum likelihood extended gradient algorithm to estimate the parameter vectors of these subsystems. By combining the maximum likelihood principle and the data filtering technique, the proposed algorithms are effective and have computational advantages over existing estimation algorithms. Finally, a numerical simulation example is given to support the developed methods and to show their effectiveness. |
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| Keywords: | Corresponding author at: School of Internet of Things Engineering Jiangnan University Wuxi 214122 PR China |
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