Parameter estimation plays a critical role for the application and development of S-shaped growth model in the agricultural sciences and others.In this paper,a modified particle swarm optimization algorithm based on t...Parameter estimation plays a critical role for the application and development of S-shaped growth model in the agricultural sciences and others.In this paper,a modified particle swarm optimization algorithm based on the diffusion phenomenon（DPPSO） was employed to estimate the parameters for this model.Under the sense of least squares,the parameter estimation problem of S-shaped growth model,taking the Gompertz and Logistic models for example,is transformed into a multi-dimensional function optimization problem.The results show that the DPPSO algorithm can effectively estimate the parameters of the S-shaped growth model.展开更多
The segmented filters, based on spectral cutting, proved their efficiency for the multi-correlation. In this article we propose an optimisation of this cutting according to a new error diffusion method.
Purpose–The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems.The proposed algorithms are experimented in this study t...Purpose–The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems.The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations.In addition,the models are tested for nonlinear systems with different noise conditions.In a nutshell,the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.Design/methodology/approach–Population-based evolutionary algorithms such as genetic algorithm(GA),particle swarm optimization(PSO)and cat swarm optimization(CSO)are implemented in a distributed form to address the system identification problem having distributed input data.Out of different distributed approaches mentioned in the literature,the study has considered incremental and diffusion strategies.Findings–Performances of the proposed distributed learning-based algorithms are compared for different noise conditions.The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate,but incremental CSO is slightly superior to diffusion CSO.Originality/value–This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems.Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.展开更多
基金Supported by the National Natural Science Foundation of China (61070009)the National Science and Technology Support Plan (2012BAH25F02)+2 种基金the Project of Jingdezhen Science and Technology Bureau (2011-1-47)the National Natural Science Foundation of Jiangxi Province (2009GZS0065)the Youth Science Foundation of Jiangxi Provincial Department of Education (GJJ12514)
文摘Parameter estimation plays a critical role for the application and development of S-shaped growth model in the agricultural sciences and others.In this paper,a modified particle swarm optimization algorithm based on the diffusion phenomenon（DPPSO） was employed to estimate the parameters for this model.Under the sense of least squares,the parameter estimation problem of S-shaped growth model,taking the Gompertz and Logistic models for example,is transformed into a multi-dimensional function optimization problem.The results show that the DPPSO algorithm can effectively estimate the parameters of the S-shaped growth model.
文摘The segmented filters, based on spectral cutting, proved their efficiency for the multi-correlation. In this article we propose an optimisation of this cutting according to a new error diffusion method.
文摘Purpose–The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems.The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations.In addition,the models are tested for nonlinear systems with different noise conditions.In a nutshell,the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.Design/methodology/approach–Population-based evolutionary algorithms such as genetic algorithm(GA),particle swarm optimization(PSO)and cat swarm optimization(CSO)are implemented in a distributed form to address the system identification problem having distributed input data.Out of different distributed approaches mentioned in the literature,the study has considered incremental and diffusion strategies.Findings–Performances of the proposed distributed learning-based algorithms are compared for different noise conditions.The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate,but incremental CSO is slightly superior to diffusion CSO.Originality/value–This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems.Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.
