研究多站点传送带给料生产加工站(Conveyor-serviced production station,CSPS)系统的最优控制问题,其优化目标是通过合理选择每个CSPS的Look-ahead控制策略,实现整个系统的工件处理率最大.本文首先根据多Agent系统的反应扩散思想,对每...研究多站点传送带给料生产加工站(Conveyor-serviced production station,CSPS)系统的最优控制问题,其优化目标是通过合理选择每个CSPS的Look-ahead控制策略,实现整个系统的工件处理率最大.本文首先根据多Agent系统的反应扩散思想,对每个Agent的原始性能函数进行改进,引入了具有扩散功能的局域信息交互项(原始项看作具有反应功能);并运用性能势理论,构建一种适用于平均和折扣两种性能准则的Wolf-PHC多Agent学习算法,以求解决策时刻不同步的多站点的协作Look-ahead控制策略.最后,论文通过仿真实验验证了该算法的有效性,学习结果表明,通过性能函数的改进,各工作站的负载平衡性得到改善,整个系统的工件处理率也明显提高.展开更多
A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
文摘研究多站点传送带给料生产加工站(Conveyor-serviced production station,CSPS)系统的最优控制问题,其优化目标是通过合理选择每个CSPS的Look-ahead控制策略,实现整个系统的工件处理率最大.本文首先根据多Agent系统的反应扩散思想,对每个Agent的原始性能函数进行改进,引入了具有扩散功能的局域信息交互项(原始项看作具有反应功能);并运用性能势理论,构建一种适用于平均和折扣两种性能准则的Wolf-PHC多Agent学习算法,以求解决策时刻不同步的多站点的协作Look-ahead控制策略.最后,论文通过仿真实验验证了该算法的有效性,学习结果表明,通过性能函数的改进,各工作站的负载平衡性得到改善,整个系统的工件处理率也明显提高.
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
