Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a ta...Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.展开更多
Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating f...Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear.Due to the complexity and random nature of the process,existing methods of compensating for such wear usually involve off-line prediction.This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function(RBF) network.Data gained from an orthogonal experiment were used to provide training samples for the RBF network.The model established was used to forecast the electrode wear,making it possible to calculate the real-time tool wear in the milling EDM process and,to lay the foundations for dynamic compensation of the electrode wear on-line.This paper demonstrates that by using this model prediction errors can be controlled within 8%.展开更多
In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with tradition...In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with traditional models to predict rolling f orce was proposed. The off-line simulation indicates that the predicted results are much more accurate than that with traditional models.展开更多
Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the p...Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow activities.Experimental results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.展开更多
The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are us...The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.展开更多
This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising fie...This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series展开更多
This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was t...This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.展开更多
文摘Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.
基金the National High Technology Research and Development Program (863) of China(No. 2007AA04Z345)the National Natural Science Foundation of China (No. 50679041)the Foundation of Heilongjiang Science and Technology Committee(No. GA06A501)
文摘Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear.Due to the complexity and random nature of the process,existing methods of compensating for such wear usually involve off-line prediction.This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function(RBF) network.Data gained from an orthogonal experiment were used to provide training samples for the RBF network.The model established was used to forecast the electrode wear,making it possible to calculate the real-time tool wear in the milling EDM process and,to lay the foundations for dynamic compensation of the electrode wear on-line.This paper demonstrates that by using this model prediction errors can be controlled within 8%.
基金National Natural Science Foundation ofChina(No.60374011)
文摘In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with traditional models to predict rolling f orce was proposed. The off-line simulation indicates that the predicted results are much more accurate than that with traditional models.
基金Supported by the European Union through the IST-034601 edutain@grid project
文摘Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow activities.Experimental results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.
文摘The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.
文摘This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series
基金The National High Technology Research and Development Program of China (863 Program) (No.2003AA517020)
文摘This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
