Echo state network （ESN） has become one of the most popular recurrent neural networks （RNN） for its good prediction performance of non-linear time series and simple training process. But several problems still pre...Echo state network （ESN） has become one of the most popular recurrent neural networks （RNN） for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model （MCESN） was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model （WMCESN） to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol （TCP） and user datagram protocol （UDP） dataset to prove that our model can deal with the data packet bit prediction problem well.展开更多
针对多尺度时间序列各尺度发展趋势及整体预测问题,建立小波分解回声状态网络预测模型(wavelet decomposi-tion and echo state networks,WDESN),根据各尺度的不同性质选取与之相匹配的回声状态网络模型(echo state networks,ESN),同时...针对多尺度时间序列各尺度发展趋势及整体预测问题,建立小波分解回声状态网络预测模型(wavelet decomposi-tion and echo state networks,WDESN),根据各尺度的不同性质选取与之相匹配的回声状态网络模型(echo state networks,ESN),同时,通过在各尺度条件下引入权值系数实现预测分量最优整合,提高整体预测精度。预测带噪多尺度正弦序列实验表明:WDESN模型与ESN、支持向量机及BP神经网络模型相比预测精度较高。目前,该模型已成功用于移动通信话务量的预测,并满足了现实系统的精度要求。展开更多
This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation com...This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.展开更多
基金supported by the National Natural Science Foundation of China (61201153)the National Basic Research Program of China (2012CB315805)the National Key Science and Technology Projects (2010ZX03004-002-02)
文摘Echo state network （ESN） has become one of the most popular recurrent neural networks （RNN） for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model （MCESN） was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model （WMCESN） to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol （TCP） and user datagram protocol （UDP） dataset to prove that our model can deal with the data packet bit prediction problem well.
文摘针对多尺度时间序列各尺度发展趋势及整体预测问题,建立小波分解回声状态网络预测模型(wavelet decomposi-tion and echo state networks,WDESN),根据各尺度的不同性质选取与之相匹配的回声状态网络模型(echo state networks,ESN),同时,通过在各尺度条件下引入权值系数实现预测分量最优整合,提高整体预测精度。预测带噪多尺度正弦序列实验表明:WDESN模型与ESN、支持向量机及BP神经网络模型相比预测精度较高。目前,该模型已成功用于移动通信话务量的预测,并满足了现实系统的精度要求。
基金supported by National Natural Science Foundation of China(No.61034005)
文摘This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.
