为能够快速、无损检测专用煎炸油的极性组分含量,采集不同煎炸时间下煎炸油样本的拉曼光谱图。为建立稳定性高、误差小、精度高的模型,研究不同预处理方法对模型效果的影响,建立相应的偏最小二乘回归模型以选择最优的光谱预处理方法。...为能够快速、无损检测专用煎炸油的极性组分含量,采集不同煎炸时间下煎炸油样本的拉曼光谱图。为建立稳定性高、误差小、精度高的模型,研究不同预处理方法对模型效果的影响,建立相应的偏最小二乘回归模型以选择最优的光谱预处理方法。结果表明:标准正态变换处理后的偏最小二乘模型最优,预测均方根误差(root mean square error of prediction,RMSEP)为1.18,决定系数R2为0.9404。其次,将标准正态变换处理后的光谱数据分别建立误差反向传播(error back propagation,BP)算法和径向基函数算法神经网络模型,通过比较稳定性以及误差大小,得出采集到的拉曼光谱经过标准正态变换处理后采用BP神经网络建立的模型效果最好,RMSEP为0.0326,R2为0.972。该方法可以用作专用煎炸油极性组分的快速分析。展开更多
To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information crite...To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.展开更多
The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a contro...The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.展开更多
文摘为能够快速、无损检测专用煎炸油的极性组分含量,采集不同煎炸时间下煎炸油样本的拉曼光谱图。为建立稳定性高、误差小、精度高的模型,研究不同预处理方法对模型效果的影响,建立相应的偏最小二乘回归模型以选择最优的光谱预处理方法。结果表明:标准正态变换处理后的偏最小二乘模型最优,预测均方根误差(root mean square error of prediction,RMSEP)为1.18,决定系数R2为0.9404。其次,将标准正态变换处理后的光谱数据分别建立误差反向传播(error back propagation,BP)算法和径向基函数算法神经网络模型,通过比较稳定性以及误差大小,得出采集到的拉曼光谱经过标准正态变换处理后采用BP神经网络建立的模型效果最好,RMSEP为0.0326,R2为0.972。该方法可以用作专用煎炸油极性组分的快速分析。
基金Project(41374118)supported by the National Natural Science Foundation,ChinaProject(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China+3 种基金Project(2015M580700)supported by the China Postdoctoral Science Foundation,ChinaProject(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(2015JC3067)supported by the Hunan Provincial Science and Technology Program,ChinaProject(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.
文摘The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.
