针对网络控制系统(networked control system,NCS)中随机时延导致系统性能下降的问题,利用粒子群优化(particle swarm optimization,PSO)的最小二乘支持向量机(least square support vector machine,LSSVM)建立NCS中随机时延预测模型,...针对网络控制系统(networked control system,NCS)中随机时延导致系统性能下降的问题,利用粒子群优化(particle swarm optimization,PSO)的最小二乘支持向量机(least square support vector machine,LSSVM)建立NCS中随机时延预测模型,精确预测未来时刻的时延;同时利用该预测算法预测的时延通过快速隐式广义预测控制算法对NCS随机时延进行补偿。仿真结果表明,PSO优化的LS-SVM算法对随机时延具有较高的预测精度,同时快速隐式广义预测控制算法可使系统的输出很好地跟踪参考轨迹,保证系统良好的控制效果。展开更多
In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For...In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For this purpose, in the first step, SSL was predicted via ad hoc LSSVM and Artificial Neural Network （ANN） models; then, streamflow and SSL data were decomposed into sub- signals via wavelet, and these decomposed sub-time series were imposed to LSSVM and ANN to simulate discharge-SSL relationship. Finally, the ability of WLSSVM was compared with other models in multi- step-ahead SSL predictions. The results showed that in daily SSL prediction, LSSVM has better outcomes with Determination Coefficient （DC）=o.92 than ad hoc ANN with DC=o.88. However unlike daily SSL, in monthly modeling, ANN has a bit accurate upshot. WLSSVM and wavelet-based ANN （WANN） models showed same consequences in daily and different in monthly SSL predictions, and adding wavelet led to more accuracy of LSSVM and ANN. Furthermore, conjunction of wavelet to LSSVM and ANN evaluated via multi-step-ahead SSL predictions and, e.g., DCLssVM=0.4 was increased to the DCwLsSVM=0.71 in 7- day ahead SSL prediction. In addition, WLSSVM outperformed WANN by increment of time horizon prediction.展开更多
文摘针对网络控制系统(networked control system,NCS)中随机时延导致系统性能下降的问题,利用粒子群优化(particle swarm optimization,PSO)的最小二乘支持向量机(least square support vector machine,LSSVM)建立NCS中随机时延预测模型,精确预测未来时刻的时延;同时利用该预测算法预测的时延通过快速隐式广义预测控制算法对NCS随机时延进行补偿。仿真结果表明,PSO优化的LS-SVM算法对随机时延具有较高的预测精度,同时快速隐式广义预测控制算法可使系统的输出很好地跟踪参考轨迹,保证系统良好的控制效果。
基金supported by the University of Tabriz under grant No. 1117394325
文摘In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For this purpose, in the first step, SSL was predicted via ad hoc LSSVM and Artificial Neural Network （ANN） models; then, streamflow and SSL data were decomposed into sub- signals via wavelet, and these decomposed sub-time series were imposed to LSSVM and ANN to simulate discharge-SSL relationship. Finally, the ability of WLSSVM was compared with other models in multi- step-ahead SSL predictions. The results showed that in daily SSL prediction, LSSVM has better outcomes with Determination Coefficient （DC）=o.92 than ad hoc ANN with DC=o.88. However unlike daily SSL, in monthly modeling, ANN has a bit accurate upshot. WLSSVM and wavelet-based ANN （WANN） models showed same consequences in daily and different in monthly SSL predictions, and adding wavelet led to more accuracy of LSSVM and ANN. Furthermore, conjunction of wavelet to LSSVM and ANN evaluated via multi-step-ahead SSL predictions and, e.g., DCLssVM=0.4 was increased to the DCwLsSVM=0.71 in 7- day ahead SSL prediction. In addition, WLSSVM outperformed WANN by increment of time horizon prediction.
