针对水下传感器网络的多用户干扰大和空间复用率低的问题进行了研究,提出采用AR模型预测信道未来状态,然后基于信道预测完成功率控制的算法,从而减小信道时空不确定性的影响。该算法利用随机几何理论,建立SINR(signal to interference-p...针对水下传感器网络的多用户干扰大和空间复用率低的问题进行了研究,提出采用AR模型预测信道未来状态,然后基于信道预测完成功率控制的算法,从而减小信道时空不确定性的影响。该算法利用随机几何理论,建立SINR(signal to interference-plus-noise ratio)模型,分析接收端的累积干扰状态,然后发送端在信道预测的基础上,以最小化网络中断概率为目标调整发送功率。实验仿真结果表明,基于信道预测的功率控制(power control based on predicted channel state,PCBPC)算法降低了网络能耗,提高了网络空间复用率。与NPC(nonpower control)算法相比,PCBPC算法在典型场景下将中断概率降低了14%,将网络功耗降低了33.3%,提高了空间复用率。展开更多
As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet...As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.展开更多
利用小波分析预测方法对金融数据—股票收盘价这一典型的非平稳时间序列进行预测.使用M a llat小波分解算法对数据进行分解,对分解后的数据进行平滑处理,然后再进行重构,而重构之后的数据就成为近似意义的平稳时间序列,这样就得到了原...利用小波分析预测方法对金融数据—股票收盘价这一典型的非平稳时间序列进行预测.使用M a llat小波分解算法对数据进行分解,对分解后的数据进行平滑处理,然后再进行重构,而重构之后的数据就成为近似意义的平稳时间序列,这样就得到了原始数据的近似信号,再应用传统时间序列预测方法对重构后的数据进行预测,将预测结果与实际值,以及和传统预测方法预测结果比较,小波分析方法预测效果更为理想.展开更多
Polar motion depicts the slow changes in the locations of the poles due to the earth's internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction.T...Polar motion depicts the slow changes in the locations of the poles due to the earth's internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction.Through statistical analysis of the time series of the LS+AR model's short-term prediction residuals,we found that there is a good correlation of model prediction residuals between adjacent terms.These indicate that the preceding model prediction residuals and experiential adjustment matrixes can be used to correct the next prediction results,thereby forming a new LS+AR model with additional error correction that applies to polar motion prediction.Simulated predictions using this new model revealed that the proposed method can improve the accuracy and reliability of polar motion prediction.In fact,the accuracies of ultra short-term and short-term predictions using the new model were equal to the international best level at present.展开更多
文摘针对水下传感器网络的多用户干扰大和空间复用率低的问题进行了研究,提出采用AR模型预测信道未来状态,然后基于信道预测完成功率控制的算法,从而减小信道时空不确定性的影响。该算法利用随机几何理论,建立SINR(signal to interference-plus-noise ratio)模型,分析接收端的累积干扰状态,然后发送端在信道预测的基础上,以最小化网络中断概率为目标调整发送功率。实验仿真结果表明,基于信道预测的功率控制(power control based on predicted channel state,PCBPC)算法降低了网络能耗,提高了网络空间复用率。与NPC(nonpower control)算法相比,PCBPC算法在典型场景下将中断概率降低了14%,将网络功耗降低了33.3%,提高了空间复用率。
基金Supported by the National Defence Science and Industry Committee(41314020201)
文摘As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.
文摘利用小波分析预测方法对金融数据—股票收盘价这一典型的非平稳时间序列进行预测.使用M a llat小波分解算法对数据进行分解,对分解后的数据进行平滑处理,然后再进行重构,而重构之后的数据就成为近似意义的平稳时间序列,这样就得到了原始数据的近似信号,再应用传统时间序列预测方法对重构后的数据进行预测,将预测结果与实际值,以及和传统预测方法预测结果比较,小波分析方法预测效果更为理想.
基金supported by the National Natural Science Foundation of China(Grant Nos.41021061&41174012)
文摘Polar motion depicts the slow changes in the locations of the poles due to the earth's internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction.Through statistical analysis of the time series of the LS+AR model's short-term prediction residuals,we found that there is a good correlation of model prediction residuals between adjacent terms.These indicate that the preceding model prediction residuals and experiential adjustment matrixes can be used to correct the next prediction results,thereby forming a new LS+AR model with additional error correction that applies to polar motion prediction.Simulated predictions using this new model revealed that the proposed method can improve the accuracy and reliability of polar motion prediction.In fact,the accuracies of ultra short-term and short-term predictions using the new model were equal to the international best level at present.
