Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices ...Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.展开更多
A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper pr...A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.展开更多
As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot ...As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot issue in the field of industrial automation and intelligence.In order to further improve the timeliness and accuracy of the flame detection network,a radial basis function(RBF)flame detection network based on particle swarm optimization(PSO)algorithm is proposed.First,the proposed algorithm initializes the speed and position parameters of the particles.Then,the parameters of the particles are mapped to the RBF flame detection network.Finally,the algorithm is iteratively updated to obtain the global optimal solution.The PSO-RBF flame detection algorithm adopts a flame sample collection method similar to back propagation(BP)flame detection algorithm,and further improves the collection efficiency.The experimental results show that the PSO-RBF flame detection network has good accuracy and faster convergence speed in the given data samples.In the flame data samples,the detection accuracy of the PSO-RBF flame detection algorithm reaches 90.5%.展开更多
基金Project (Nos. 40571115 and 40271078) supported by the National Natural Science Foundation of China
文摘Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.
基金Project supported bY the National Natural Science Foundation of China （Grant No.50375085）, and the Natural Science Foundation of Shandong Province （Grant No.Y2002F13）
文摘A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.
基金Supported by Shaanxi Province Key Research and Development Project(No.2021GY-280,2021GY-029)Shaanxi Province Natural Science Basic Research Program Project(No.2021JM-459)National Natural Science Foundation of China(No.61834005,61772417,61802304).
文摘As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot issue in the field of industrial automation and intelligence.In order to further improve the timeliness and accuracy of the flame detection network,a radial basis function(RBF)flame detection network based on particle swarm optimization(PSO)algorithm is proposed.First,the proposed algorithm initializes the speed and position parameters of the particles.Then,the parameters of the particles are mapped to the RBF flame detection network.Finally,the algorithm is iteratively updated to obtain the global optimal solution.The PSO-RBF flame detection algorithm adopts a flame sample collection method similar to back propagation(BP)flame detection algorithm,and further improves the collection efficiency.The experimental results show that the PSO-RBF flame detection network has good accuracy and faster convergence speed in the given data samples.In the flame data samples,the detection accuracy of the PSO-RBF flame detection algorithm reaches 90.5%.
