Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory |
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| Institution: | 1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, PR China;2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;1. Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh 492010, India;2. Department of Electronics and Communication Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal 713209, India;1. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;2. Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, University of Electronic and Technology of China, Chengdu 611731, China;3. Department of Computer Science and Technology, Faculty of Science and Engineering, University of Hull, HU6 7RX, UK;1. College of Electrical Engineering, North China University of Science and Technology, Tangshan, China;2. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China;3. Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea;1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;2. Department of Electrical and Electronic Engineering, Shiraz University of Technology, Modarres Blvd., P.O. Box: 71555-313, Shiraz, Iran;3. School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;4. School of Information and Communication Engineering, Hainan University, Haikou 570228, Hainan, China;1. School of Mathematical Sciences, University of Jinan, Jinan 250022, China;2. College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China |
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| Abstract: | This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems. |
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