In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-B...In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.展开更多
Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests ...Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests forecasting using the method of neural network based on fuzzy clustering was proposed in this experiment. The simulation results demonstrated that the method was simple and practical and could forecast pests fast and accurately, particularly, the method could obtain good results with few samples and samples correlation.展开更多
A new analytical method using Back-Propagation （BP） artificial neural networks and spectrophotometry for simultaneous determination of calcium and magnesium in tap water, the Yellow River water and seawater is estab...A new analytical method using Back-Propagation （BP） artificial neural networks and spectrophotometry for simultaneous determination of calcium and magnesium in tap water, the Yellow River water and seawater is established. By condition experiment, the optimum analytical conditions for calcium, magnesium and Arsenazo （Ⅲ） color reactions are obtained. Levenberg- Marquart （L-M） algorithm is used for calculation in BP neural network. The topological structure of three-layer BP ANN network architecture is chosen as 11-10-2 （nodes）. The initial value of gradient coefficient μ is fixed at 0.001 and the increase factor and reduction factor of kt take the default values of the system. The data are processed by computers with our own programs written in MATLAB 7.0. The relative standard deviations of the calculated results for calcium and magnesium are 2.31% and 2.14%, respectively. The results of standard addition method show that the recoveries of calcium and magnesium are 103.6% and 100.8% in the tap water, 103.2% and 96.6% in the Yellow River water （Lijin district of Shandong Province）, and 98.8%-103.3% and 98.43%-103.4% in seawater from Jiaozhou Bay of Qingdao. It is found that 14 common cations and anions do not interfere with the determination of calcium and magnesium under the optimum experimental conditions. The comparative experiments do not show any obvious differ- ence between the results obtained by this new method and those obtained by the classical complexometric titration method in seawater medium. This method exhibits good reproducibility and high accuracy in the determination of calcium and magnesium and can be used for the simultaneous determination of Ca^2＋ and Mg^2＋ in tap water and natural water.展开更多
In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are...In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.展开更多
The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors w...The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.展开更多
基金Project(50175110) supported by the National Natural Science Foundation of ChinaProject(2009bsxt019) supported by the Graduate Degree Thesis Innovation Foundation of Central South University, China
文摘In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.
基金Supported by Guangxi Science Research and Technology Explora-tion Plan Project(0815001-10)~~
文摘Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests forecasting using the method of neural network based on fuzzy clustering was proposed in this experiment. The simulation results demonstrated that the method was simple and practical and could forecast pests fast and accurately, particularly, the method could obtain good results with few samples and samples correlation.
文摘A new analytical method using Back-Propagation （BP） artificial neural networks and spectrophotometry for simultaneous determination of calcium and magnesium in tap water, the Yellow River water and seawater is established. By condition experiment, the optimum analytical conditions for calcium, magnesium and Arsenazo （Ⅲ） color reactions are obtained. Levenberg- Marquart （L-M） algorithm is used for calculation in BP neural network. The topological structure of three-layer BP ANN network architecture is chosen as 11-10-2 （nodes）. The initial value of gradient coefficient μ is fixed at 0.001 and the increase factor and reduction factor of kt take the default values of the system. The data are processed by computers with our own programs written in MATLAB 7.0. The relative standard deviations of the calculated results for calcium and magnesium are 2.31% and 2.14%, respectively. The results of standard addition method show that the recoveries of calcium and magnesium are 103.6% and 100.8% in the tap water, 103.2% and 96.6% in the Yellow River water （Lijin district of Shandong Province）, and 98.8%-103.3% and 98.43%-103.4% in seawater from Jiaozhou Bay of Qingdao. It is found that 14 common cations and anions do not interfere with the determination of calcium and magnesium under the optimum experimental conditions. The comparative experiments do not show any obvious differ- ence between the results obtained by this new method and those obtained by the classical complexometric titration method in seawater medium. This method exhibits good reproducibility and high accuracy in the determination of calcium and magnesium and can be used for the simultaneous determination of Ca^2＋ and Mg^2＋ in tap water and natural water.
基金Supported by National Natural Science Foundation of China (No. 60573172)
文摘In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.
基金Project supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2015SK20823)supported by Science and Technology Project of Hunan Province,China+2 种基金Project(15A001)supported by Scientific Research Fund of Hunan Provincial Education Department,ChinaProject(CX2015B372)supported by Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject supported by Innovation Experiment Program for University Students of Changsha University of Science and Technology,China
文摘The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.
