A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant i...A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.展开更多
Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlin...Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.展开更多
Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is intr...Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.展开更多
针对风电场风功率预测问题,利用历史风功率、气象数据和测风塔实时数据等相关信息,提出了带有批特征的混核最小二乘支持向量机(Hybrid kernel least squares support vector machine,HKLSSVM)方法,建立风电场风功率预测模型.为了增强模...针对风电场风功率预测问题,利用历史风功率、气象数据和测风塔实时数据等相关信息,提出了带有批特征的混核最小二乘支持向量机(Hybrid kernel least squares support vector machine,HKLSSVM)方法,建立风电场风功率预测模型.为了增强模型的适应性,设计改进的差分进化算法对模型参数进行优化,并利用稀疏选择方法来选取合适的训练样本集,缩短建模时间,保证预测模型精度.根据风场风机的地理位置分布情况,提出批划分的建模策略,对相近地理位置的风机进行组批,替代传统风场风功率预测方法.通过风场中实际数据进行测试,实验结果表明与其他预测方法相比,本文提出的方法能够提高预测精度和效率,减少风电波动性对电网的影响,从而提高电网的安全性和可靠性.展开更多
基金This work has been supported by the National Outstanding Youth Science Foundation of China (No. 60025308) and the Teach and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,China.
文摘A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.
基金Project supported by the National Outstanding Youth ScienceFoundation of China (No. 60025308) and the Teach and ResearchAward Program for Outstanding Young Teachers in Higher EducationInstitutions of MOE, China
文摘Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.
基金supported by the Fundamental Research Funds for the Central Universities(No.NS2016027)
文摘Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.
文摘针对风电场风功率预测问题,利用历史风功率、气象数据和测风塔实时数据等相关信息,提出了带有批特征的混核最小二乘支持向量机(Hybrid kernel least squares support vector machine,HKLSSVM)方法,建立风电场风功率预测模型.为了增强模型的适应性,设计改进的差分进化算法对模型参数进行优化,并利用稀疏选择方法来选取合适的训练样本集,缩短建模时间,保证预测模型精度.根据风场风机的地理位置分布情况,提出批划分的建模策略,对相近地理位置的风机进行组批,替代传统风场风功率预测方法.通过风场中实际数据进行测试,实验结果表明与其他预测方法相比,本文提出的方法能够提高预测精度和效率,减少风电波动性对电网的影响,从而提高电网的安全性和可靠性.
