A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization
Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2016/3795961 |
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| author | Shaolong Chen Renyu Yang Renhuan Yang Liu Yang Xiuzeng Yang Chuangbiao Xu Baoguo Xu Huatao Zhang Yaosheng Lu Weiping Liu |
| author_facet | Shaolong Chen Renyu Yang Renhuan Yang Liu Yang Xiuzeng Yang Chuangbiao Xu Baoguo Xu Huatao Zhang Yaosheng Lu Weiping Liu |
| author_sort | Shaolong Chen |
| collection | DOAJ |
| description | Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems. |
| format | Article |
| id | doaj-art-168c521240584f1ab42b0dff1559523d |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-168c521240584f1ab42b0dff1559523d2025-02-03T01:11:25ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/37959613795961A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm OptimizationShaolong Chen0Renyu Yang1Renhuan Yang2Liu Yang3Xiuzeng Yang4Chuangbiao Xu5Baoguo Xu6Huatao Zhang7Yaosheng Lu8Weiping Liu9College of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaSchool of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaDepartment of Physics and Electronic Engineering, Guangxi Normal University for Nationalities, Chongzuo 532200, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaKey Laboratory of Astronomical Optics & Technology, Nanjing Institute of Astronomical Optics & Technology, Chinese Academy of Sciences, Nanjing 210042, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaParameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.http://dx.doi.org/10.1155/2016/3795961 |
| spellingShingle | Shaolong Chen Renyu Yang Renhuan Yang Liu Yang Xiuzeng Yang Chuangbiao Xu Baoguo Xu Huatao Zhang Yaosheng Lu Weiping Liu A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization Discrete Dynamics in Nature and Society |
| title | A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization |
| title_full | A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization |
| title_fullStr | A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization |
| title_full_unstemmed | A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization |
| title_short | A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization |
| title_sort | parameter estimation method for nonlinear systems based on improved boundary chicken swarm optimization |
| url | http://dx.doi.org/10.1155/2016/3795961 |
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