The Experts below are selected from a list of 210 Experts worldwide ranked by ideXlab platform
Zheng Wu - One of the best experts on this subject based on the ideXlab platform.
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Multi-Model Modeling of CFB Boiler Bed Temperature System Based on Principal Component Analysis
IEEE Access, 2020Co-Authors: Yue Zhang, Bingrong Zhang, Zheng WuAbstract:Multi-model modeling is an important method for solving the modeling problem of complex non-linear systems. However, there is no such good classification principle of its sub-models' time span, and the division of sub-windows affects the accuracy of the model, as well as the cost of computing. Through analysis of the characteristics of the bed Temperature Object, the principal component analysis was introduced into the method of designing the time span division of the sub-windows. The dividing points of the time span of the sub-windows were determined by piecewise analysis, rolling merging, and cyclic validation. The problem with this is that the sub-models in different windows may be different. Aiming at the problems of modeling in the sub-windows, the contribution rates of variables to the principal components were analyzed, the independent variables that play a major role in the dependent variables in the sub-windows were determined with the results of PCA, and the regression models in the the sub-windows were identified by multivariate linear regression. Compared to the sub-window model established by the principal component regression method, the former method had higher accuracy and could better reflect the actual operation of the bed Temperature Object.
Yue Zhang - One of the best experts on this subject based on the ideXlab platform.
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Multi-Model Modeling of CFB Boiler Bed Temperature System Based on Principal Component Analysis
IEEE Access, 2020Co-Authors: Yue Zhang, Bingrong Zhang, Zheng WuAbstract:Multi-model modeling is an important method for solving the modeling problem of complex non-linear systems. However, there is no such good classification principle of its sub-models' time span, and the division of sub-windows affects the accuracy of the model, as well as the cost of computing. Through analysis of the characteristics of the bed Temperature Object, the principal component analysis was introduced into the method of designing the time span division of the sub-windows. The dividing points of the time span of the sub-windows were determined by piecewise analysis, rolling merging, and cyclic validation. The problem with this is that the sub-models in different windows may be different. Aiming at the problems of modeling in the sub-windows, the contribution rates of variables to the principal components were analyzed, the independent variables that play a major role in the dependent variables in the sub-windows were determined with the results of PCA, and the regression models in the the sub-windows were identified by multivariate linear regression. Compared to the sub-window model established by the principal component regression method, the former method had higher accuracy and could better reflect the actual operation of the bed Temperature Object.
Cheng Qi-ming - One of the best experts on this subject based on the ideXlab platform.
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A Simulation Research on the Power Station Superheated Steam Temperature Neural Network PID Control System
Journal of Shanghai Institute & Electric Power, 2020Co-Authors: Cheng Qi-mingAbstract:In order to overcome the large delay and the uncertainty of the main-stream Temperature Object in fossil-fired power station, a neural PID Temperature control system based on RBF neural network is proposed. It contains two neural netwoks: system-online identifier and neural PID controller. The simulation results show that the designed system has good position tracking propery, adaptability to overcome disturbances and robustness.
Bingrong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Multi-Model Modeling of CFB Boiler Bed Temperature System Based on Principal Component Analysis
IEEE Access, 2020Co-Authors: Yue Zhang, Bingrong Zhang, Zheng WuAbstract:Multi-model modeling is an important method for solving the modeling problem of complex non-linear systems. However, there is no such good classification principle of its sub-models' time span, and the division of sub-windows affects the accuracy of the model, as well as the cost of computing. Through analysis of the characteristics of the bed Temperature Object, the principal component analysis was introduced into the method of designing the time span division of the sub-windows. The dividing points of the time span of the sub-windows were determined by piecewise analysis, rolling merging, and cyclic validation. The problem with this is that the sub-models in different windows may be different. Aiming at the problems of modeling in the sub-windows, the contribution rates of variables to the principal components were analyzed, the independent variables that play a major role in the dependent variables in the sub-windows were determined with the results of PCA, and the regression models in the the sub-windows were identified by multivariate linear regression. Compared to the sub-window model established by the principal component regression method, the former method had higher accuracy and could better reflect the actual operation of the bed Temperature Object.
Song Yanping - One of the best experts on this subject based on the ideXlab platform.
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The Application of RBF-NN Based PID Controller in Superheated Steam Temperature Control System
Shaanxi Electric Power, 2020Co-Authors: Song YanpingAbstract:By combining the RBF neural network with the general PID controller,a new superheated steam Temperature control sys- tem is derived.This scheme can over the large delay and the uncertainty of superheated steam Temperature Object in supercritieal power unit.Through simulation in various situations,it validates that the control quality and the robustness of this control system ap- parently are superiors to the general PID system.
