A novel method of incorporating generalized predictive control (GPC) algorithms based on quantitative feedback theory (QFT) principles is proposed for solving the feedback control problem of the highly uncertain and c...A novel method of incorporating generalized predictive control (GPC) algorithms based on quantitative feedback theory (QFT) principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output (MIMO) plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output (SISO) generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.展开更多
A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross...A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output MIMO plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output SISO generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.展开更多
Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problem...Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.展开更多
Long-term prediction is still a difficult problem in data mining.People usually use various kinds of methods of Recurrent Neural Network to predict.However,with the increase of the prediction step,the accuracy of pred...Long-term prediction is still a difficult problem in data mining.People usually use various kinds of methods of Recurrent Neural Network to predict.However,with the increase of the prediction step,the accuracy of prediction decreases rapidly.In order to improve the accuracy of long-term prediction,we propose a framework Variational Auto-Encoder Conditional Generative Adversarial Network(VAECGAN).Our model is divided into three parts.The first part is the encoder net,which can encode the exogenous sequence into latent space vectors and fully save the information carried by the exogenous sequence.The second part is the generator net which is responsible for generating prediction data.In the third part,the discriminator net is used to classify and feedback,adjust data generation and improve prediction accuracy.Finally,extensive empirical studies tested with five real-world datasets(NASDAQ,SML,Energy,EEG,KDDCUP)demonstrate the effectiveness and robustness of our proposed approach.展开更多
基金Supported by the National Natural Science Foundation of China (No.60374037, No.60574036), the Program for New Century Excellent Talents in Education Ministry (NCET), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20050055013).
文摘A novel method of incorporating generalized predictive control (GPC) algorithms based on quantitative feedback theory (QFT) principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output (MIMO) plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output (SISO) generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.
基金the National Natural Science Foundation of China (No.60374037, No.60574036)the Program for New CenturyExcellent Talents in Education Ministry (NCET)the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013)
文摘A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output MIMO plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output SISO generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.
基金supported by the National Natural Science Foundation of China (51479151,61403288)。
文摘Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.
基金the Youth Talent Star of Institute of Information Engineering,Chinese Academy of Sciences(Y7Z0091105)This work was supported in part by National Natural Science Foundation of China under Grant 61771469.
文摘Long-term prediction is still a difficult problem in data mining.People usually use various kinds of methods of Recurrent Neural Network to predict.However,with the increase of the prediction step,the accuracy of prediction decreases rapidly.In order to improve the accuracy of long-term prediction,we propose a framework Variational Auto-Encoder Conditional Generative Adversarial Network(VAECGAN).Our model is divided into three parts.The first part is the encoder net,which can encode the exogenous sequence into latent space vectors and fully save the information carried by the exogenous sequence.The second part is the generator net which is responsible for generating prediction data.In the third part,the discriminator net is used to classify and feedback,adjust data generation and improve prediction accuracy.Finally,extensive empirical studies tested with five real-world datasets(NASDAQ,SML,Energy,EEG,KDDCUP)demonstrate the effectiveness and robustness of our proposed approach.
