Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a...Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.展开更多
In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem...In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem, an improved least squares support vector regression （LSSVR） model with multiple-kernel is proposed and the model is applied to the nonlinear separable system identification. This method utilizes the excellent nonlinear mapping ability of Morlet wavelet kernel function and combines the state and control variables information into a kernel matrix. Using the composite wavelet kernel, the LSSVR includes two nonlinear functions, whose variables are the state variables and the control ones respectively, in this way, the regression function can gain better nonlinear mapping ability, and it can simulate almost any curve in quadratic continuous integral space. Then, they are used to identify the two functions in the separable nonlinear dynamic system. Simulation results show that the multiple-kernel LSSVR method can greatly improve the identification accuracy than the single kernel method, and the Morlet wavelet kernel is more efficient than the other kernels.展开更多
Glycoprotein from Ginkgo biloba kernel may be allergic. In this paper, the allergic proteins were identified with Western blotting, and the 32 kDa glycoprotein was purified with ion exchange chromatography and gel chr...Glycoprotein from Ginkgo biloba kernel may be allergic. In this paper, the allergic proteins were identified with Western blotting, and the 32 kDa glycoprotein was purified with ion exchange chromatography and gel chromatography. With Western blotting, there were 3 allergic proteins with molecular weight 21, 32, and 36 kDa. With SDS-PAGE analysis and measurements of the protein and sugar contents, the isolation and purification technology of 32 kDa was confirmed. Ginkgo crude protein extract was precipitated with ammonium sulphate （saturation gradient： 40-80%）. The precipitate was purified by chromatography with DEAE-cellulose 52, then chromatography with Sephadex G-200 and the target glycoprotein was finally obtained. The analysis results showed the molecule of the glycoprotein was 32.12 kDa and the ratio of protein to sugar was 20.56：1. In conclusion, the purification method could be used in preparation of the glycoprotein, and the study could provide a basis for the further research of the glycoprotein.展开更多
A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal o...A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.展开更多
This paper discusses closed-loop identification of unstable systems.In particular,wefirst apply the joint input–output identification method and then convert the identification problem of unstable systems into that of st...This paper discusses closed-loop identification of unstable systems.In particular,wefirst apply the joint input–output identification method and then convert the identification problem of unstable systems into that of stable systems,which can be tackled by using kernel-based regularization methods.We propose to identify two transfer functions by kernel regularization,the one from the reference signal to the input,and the one from the reference signal to the output.Since these transfer functions are stable,kernel regularization methods can construct their accurate models.Then the model of unstable system is constructed by ratio of these functions.The effectiveness of the proposed method is demonstrated by a numerical example and a practical experiment with a DC motor.展开更多
建立1种基于最小二乘支持向量机(least squares support vector machine,LSSVM)的模糊辨识方法,根据学习样本集的模糊聚类结果,产生LSSVM的模糊核函数,并证明该模糊核函数是Mercer核函数,为LSSVM提供1种构造核函数的简便方法。此外,由...建立1种基于最小二乘支持向量机(least squares support vector machine,LSSVM)的模糊辨识方法,根据学习样本集的模糊聚类结果,产生LSSVM的模糊核函数,并证明该模糊核函数是Mercer核函数,为LSSVM提供1种构造核函数的简便方法。此外,由于所建立的模糊辨识方法在T-S模糊模型的后件参数学习过程中采用结构风险最小化准则,提高了模型的泛化能力。利用所建立的辨识方法进行热工对象逆系统模型辨识,证明了该方法的有效性。展开更多
基金Supported by the State Key Development Program for Basic Research of China （No.2002CB312200） and the National Natural Science Foundation of China （No.60574019）.
文摘Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
基金supported by the National Natural Science Foundation for Young Scientists of China(Nos.61202332,60904083)the China Postdoctoral Science Foundation(No.2012M521905)
文摘In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem, an improved least squares support vector regression （LSSVR） model with multiple-kernel is proposed and the model is applied to the nonlinear separable system identification. This method utilizes the excellent nonlinear mapping ability of Morlet wavelet kernel function and combines the state and control variables information into a kernel matrix. Using the composite wavelet kernel, the LSSVR includes two nonlinear functions, whose variables are the state variables and the control ones respectively, in this way, the regression function can gain better nonlinear mapping ability, and it can simulate almost any curve in quadratic continuous integral space. Then, they are used to identify the two functions in the separable nonlinear dynamic system. Simulation results show that the multiple-kernel LSSVR method can greatly improve the identification accuracy than the single kernel method, and the Morlet wavelet kernel is more efficient than the other kernels.
基金supported by Research Fund for the Doctoral Program of Higher Education of China(200802980004 and 20070298008)the National Natural Science Fundation of China (30872055)
文摘Glycoprotein from Ginkgo biloba kernel may be allergic. In this paper, the allergic proteins were identified with Western blotting, and the 32 kDa glycoprotein was purified with ion exchange chromatography and gel chromatography. With Western blotting, there were 3 allergic proteins with molecular weight 21, 32, and 36 kDa. With SDS-PAGE analysis and measurements of the protein and sugar contents, the isolation and purification technology of 32 kDa was confirmed. Ginkgo crude protein extract was precipitated with ammonium sulphate （saturation gradient： 40-80%）. The precipitate was purified by chromatography with DEAE-cellulose 52, then chromatography with Sephadex G-200 and the target glycoprotein was finally obtained. The analysis results showed the molecule of the glycoprotein was 32.12 kDa and the ratio of protein to sugar was 20.56：1. In conclusion, the purification method could be used in preparation of the glycoprotein, and the study could provide a basis for the further research of the glycoprotein.
基金Supported by the Natural Science Foundation of Zhejiang Province（Y1100842） the Planning Projects of General Administration of Quality Supervision Inspection and Quarantine of the People＇s Republic of China（2006QK23）
文摘A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.
文摘This paper discusses closed-loop identification of unstable systems.In particular,wefirst apply the joint input–output identification method and then convert the identification problem of unstable systems into that of stable systems,which can be tackled by using kernel-based regularization methods.We propose to identify two transfer functions by kernel regularization,the one from the reference signal to the input,and the one from the reference signal to the output.Since these transfer functions are stable,kernel regularization methods can construct their accurate models.Then the model of unstable system is constructed by ratio of these functions.The effectiveness of the proposed method is demonstrated by a numerical example and a practical experiment with a DC motor.
文摘建立1种基于最小二乘支持向量机(least squares support vector machine,LSSVM)的模糊辨识方法,根据学习样本集的模糊聚类结果,产生LSSVM的模糊核函数,并证明该模糊核函数是Mercer核函数,为LSSVM提供1种构造核函数的简便方法。此外,由于所建立的模糊辨识方法在T-S模糊模型的后件参数学习过程中采用结构风险最小化准则,提高了模型的泛化能力。利用所建立的辨识方法进行热工对象逆系统模型辨识,证明了该方法的有效性。
