As a category of recurrent neural networks,echo state networks(ESNs)have been the topic of in-depth investigations and extensive applications in a diverse array of fields,with spectacular triumphs achieved.Nevertheles...As a category of recurrent neural networks,echo state networks(ESNs)have been the topic of in-depth investigations and extensive applications in a diverse array of fields,with spectacular triumphs achieved.Nevertheless,the traditional ESN and the majority of its variants are devised in the light of the second-order statistical information of data(e.g.,variance and covariance),while more information is neglected.In the context of information theoretic learning,correntropy demonstrates the capacity to grab more information from data.Therefore,under the guidelines of the maximum correntropy criterion,this paper proposes a correntropy-based echo state network(CESN)in which the first-order and higher-order information of data is captured,promoting robustness to noise.Furthermore,an incremental learning algorithm for the CESN is presented,which has the expertise to update the CESN when new data arrives,eliminating the need to retrain the network from scratch.Finally,experiments on benchmark problems and comparisons with existing works are provided to verify the effectiveness and superiority of the proposed CESN.展开更多
Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is c...Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.展开更多
High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a s...High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape： recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron （MLP） is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition per- formance of ZRM control systems, echo state networks （ESNs） are proposed to be used. Through simulation re- sults, it is found that shape recognition performance could be improved using the proposed ESN method.展开更多
With the challenge from services diversity grows greatly,the service-oriented supporting ability is required to current high-speed passive optical network(PON) .Aimed to enhance the quality of service(Qo S) brought by...With the challenge from services diversity grows greatly,the service-oriented supporting ability is required to current high-speed passive optical network(PON) .Aimed to enhance the quality of service(Qo S) brought by diversified-services,this paper proposes an Simplified Echo State Network(SESN) Based Services Awareness scheme in High-Speed PON(Passive Optical Network) .In this proposed scheme,the ring topology is adopted in the reservoir of SESN to reduce the complexity of original Echo State Network,and system dynamics equation is introduced to keep the accuracy of SESN.According to the network architecture of 10G-EPON,a SESN Master is running in the OLT and a number of SESN Agents work in ONUs.The SESN Master plays the main function of service-awareness from the total view of various kinds services in 10G-EPON system,by fully SESN training.Then,the reservoir information of well-trained SESN in OLT will be broadcasted to all ONUs and those SESN Agents working in ONUs are allowed to conducts independent service-awareness function.Thus,resources allocation and transport policy are both determined just only in ONUs.Simulation results show that the proposed mechanism is able to better supporting ability for multiple services.展开更多
The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI ca...The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits.However,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold calculation.The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)performance.In contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using ESN.The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation.With this approach,the calculation complexity is reduced compared to the previous ESN-based approach.The proposed method achieves better BER performance compared to the previous method.The reason for this superior result is twofold.First,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available.Second,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information estimation.Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.展开更多
With the challenge of great growing of services diversity,service-oriented supporting ability is required by current high-speed passive optical network( PON). Aimed at enhancing the quality of service( Qo S) brought b...With the challenge of great growing of services diversity,service-oriented supporting ability is required by current high-speed passive optical network( PON). Aimed at enhancing the quality of service( Qo S) brought by diversified-services,this study proposes an echo state network( ESN)based multi-service awareness mechanism in 10-Gigabite ethernet passive optical network( 10GEPON). In the proposed approach,distributed architecture is adopted to realize this ESN based multi-service awareness. According to the network architecture of 10G-EPON,where a main ESN is running in OLT and a number of ESN agents works in ONUs. The main-ESN plays the main function of service-awareness from the total view of various kinds of services in 10G-EPON system,by full ESN training. Then,the reservoir information of well-trained ESN in OLT will be broadcasted to all ONUs and those ESN agents working in ONUs are allowed to conduct independent service-awareness function. Thus,resources allocation and transport policy are both determined only in ONUs. Simulation results show that the proposed mechanism is able to better support the ability of multiple services.展开更多
Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro...Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro-blems.We compared our ESN with a long short-term memory(LSTM)network by forecasting the stock data of Kweichow Moutai,a leading enterprise in China’s liquor industry.By analyzing data for 120,240,and 300 days,we generated fore-cast data for the next 40,80,and 100 days,respectively,using both ESN and LSTM.In terms of accuracy,ESN had the unique advantage of capturing non-linear data.Mean absolute error(MAE)was used to present the accuracy results.The MAEs of the data forecast by ESN were 0.024,0.024,and 0.025,which were,respectively,0.065,0.007,and 0.009 less than those of LSTM.In terms of con-vergence,ESN has a reservoir state-space structure,which makes it perform faster than other models.Root-mean-square error(RMSE)was used to present the con-vergence time.In our experiment,the RMSEs of ESN were 0.22,0.27,and 0.26,which were,respectively,0.08,0.01,and 0.12 less than those of LSTM.In terms of network structure,ESN consists only of input,reservoir,and output spaces,making it a much simpler model than the others.The proposed ESN was found to be an effective model that,compared to others,converges faster,forecasts more accurately,and builds time-series analyses more easily.展开更多
<div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservo...<div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series. </div>展开更多
火电机组在现代电力系统中承担着大量的调峰调频任务,通过运行参数建立出力预测模型有助于快速稳定地调整功率。提出一种改进的深度回声状态网络(Deep Echo State Networks,DESN)用于建立机组出力预测模型。该改进型具备可变的记忆能力...火电机组在现代电力系统中承担着大量的调峰调频任务,通过运行参数建立出力预测模型有助于快速稳定地调整功率。提出一种改进的深度回声状态网络(Deep Echo State Networks,DESN)用于建立机组出力预测模型。该改进型具备可变的记忆能力以应对调整部分运行参数作用于机组出力变化存在的延时性,并根据运行参数聚类生成输入权重进一步挖掘运行参数与出力之间的映射信息。利用华北地区某火电机组不同工作状况下的两种数据集验证了模型效果。结果表明,改进得到的KM-VML-DESN相较于深度回声状态网络、多层感知机、长短期记忆网络等具备更强的预测性能。展开更多
针对污水处理过程高度非线性、大滞后等特征,提出了一种基于回声状态网络(echo state network,ESN)模型的多变量自适应预测控制系统.首先,利用ESN建立污水处理过程的智能预测模型,该模型能够预测污水处理的输出;其次,设计污水处理过程的...针对污水处理过程高度非线性、大滞后等特征,提出了一种基于回声状态网络(echo state network,ESN)模型的多变量自适应预测控制系统.首先,利用ESN建立污水处理过程的智能预测模型,该模型能够预测污水处理的输出;其次,设计污水处理过程的ESN辨识器,将辨识器输出与实际输出的差对主控制器进行误差补偿;最后,以仿真基准模型(BSM1)为平台,采用提出的多变量自适应控制方法对溶解氧浓度和硝态氮浓度进行控制,实验结果表明,该控制方法提高了系统的自适应性和抗干扰能力,能够对溶解氧浓度和硝态氮浓度实现快速、准确跟踪.展开更多
基金supported in part by the National Natural Science Foundation of China(62176109,62476115)the Fundamental Research Funds for the Central Universities(lzujbky-2023-ey07,lzujbky-2023-it14)+1 种基金the Natural Science Foundation of Gansu Province(24JRRA488)the Supercomputing Center of Lanzhou University
文摘As a category of recurrent neural networks,echo state networks(ESNs)have been the topic of in-depth investigations and extensive applications in a diverse array of fields,with spectacular triumphs achieved.Nevertheless,the traditional ESN and the majority of its variants are devised in the light of the second-order statistical information of data(e.g.,variance and covariance),while more information is neglected.In the context of information theoretic learning,correntropy demonstrates the capacity to grab more information from data.Therefore,under the guidelines of the maximum correntropy criterion,this paper proposes a correntropy-based echo state network(CESN)in which the first-order and higher-order information of data is captured,promoting robustness to noise.Furthermore,an incremental learning algorithm for the CESN is presented,which has the expertise to update the CESN when new data arrives,eliminating the need to retrain the network from scratch.Finally,experiments on benchmark problems and comparisons with existing works are provided to verify the effectiveness and superiority of the proposed CESN.
基金supported in part by the National Natural Science Foundation of China(62176109)the CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2022-020A)+3 种基金the Natural Science Foundation of Gansu Province(21JR7RA531,22JR5RA427,22JR5RA487)the Fundamental Research Funds for the Central Universities(lzujbky-2022-kb12,lzujbky-2022-23)the Science and Technology Project of Chengguan Discrict of Lanzhou(2021-1-2)the Supercomputing Center of Lanzhou University。
文摘Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.
基金Sponsored by Korea Science and Engineering Foundation(KOSEF)Funded by Korea Government(MEST)(2010-0022521)
文摘High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape： recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron （MLP） is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition per- formance of ZRM control systems, echo state networks （ESNs） are proposed to be used. Through simulation re- sults, it is found that shape recognition performance could be improved using the proposed ESN method.
基金supported by the Science and Technology Project of State Grid Corporation of China:“Research on the Communication Architecture and Hardware-In-the-Loop Simu-lation of Real-Time Wide-Area Stability Control for Electric Power System”
文摘With the challenge from services diversity grows greatly,the service-oriented supporting ability is required to current high-speed passive optical network(PON) .Aimed to enhance the quality of service(Qo S) brought by diversified-services,this paper proposes an Simplified Echo State Network(SESN) Based Services Awareness scheme in High-Speed PON(Passive Optical Network) .In this proposed scheme,the ring topology is adopted in the reservoir of SESN to reduce the complexity of original Echo State Network,and system dynamics equation is introduced to keep the accuracy of SESN.According to the network architecture of 10G-EPON,a SESN Master is running in the OLT and a number of SESN Agents work in ONUs.The SESN Master plays the main function of service-awareness from the total view of various kinds services in 10G-EPON system,by fully SESN training.Then,the reservoir information of well-trained SESN in OLT will be broadcasted to all ONUs and those SESN Agents working in ONUs are allowed to conducts independent service-awareness function.Thus,resources allocation and transport policy are both determined just only in ONUs.Simulation results show that the proposed mechanism is able to better supporting ability for multiple services.
文摘The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits.However,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold calculation.The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)performance.In contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using ESN.The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation.With this approach,the calculation complexity is reduced compared to the previous ESN-based approach.The proposed method achieves better BER performance compared to the previous method.The reason for this superior result is twofold.First,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available.Second,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information estimation.Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.
基金Supported by the National High Technology Research and Development Programme of China(No.2012AA050804)
文摘With the challenge of great growing of services diversity,service-oriented supporting ability is required by current high-speed passive optical network( PON). Aimed at enhancing the quality of service( Qo S) brought by diversified-services,this study proposes an echo state network( ESN)based multi-service awareness mechanism in 10-Gigabite ethernet passive optical network( 10GEPON). In the proposed approach,distributed architecture is adopted to realize this ESN based multi-service awareness. According to the network architecture of 10G-EPON,where a main ESN is running in OLT and a number of ESN agents works in ONUs. The main-ESN plays the main function of service-awareness from the total view of various kinds of services in 10G-EPON system,by full ESN training. Then,the reservoir information of well-trained ESN in OLT will be broadcasted to all ONUs and those ESN agents working in ONUs are allowed to conduct independent service-awareness function. Thus,resources allocation and transport policy are both determined only in ONUs. Simulation results show that the proposed mechanism is able to better support the ability of multiple services.
基金supported by the National Natural Science Foundation of China(No.72073041)Open Foundation for the University Innovation Platform in Hunan Province(No.18K103)+2 种基金2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project(Nos.20181901CRP03,20181901CRP04,20181901CRP05)2020 Hunan Provincial Higher Education Teaching Reform Research Project(Nos.HNJG-2020-1130,HNJG-2020-1124)2020 General Project of Hunan Social Science Fund(No.20B16).
文摘Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro-blems.We compared our ESN with a long short-term memory(LSTM)network by forecasting the stock data of Kweichow Moutai,a leading enterprise in China’s liquor industry.By analyzing data for 120,240,and 300 days,we generated fore-cast data for the next 40,80,and 100 days,respectively,using both ESN and LSTM.In terms of accuracy,ESN had the unique advantage of capturing non-linear data.Mean absolute error(MAE)was used to present the accuracy results.The MAEs of the data forecast by ESN were 0.024,0.024,and 0.025,which were,respectively,0.065,0.007,and 0.009 less than those of LSTM.In terms of con-vergence,ESN has a reservoir state-space structure,which makes it perform faster than other models.Root-mean-square error(RMSE)was used to present the con-vergence time.In our experiment,the RMSEs of ESN were 0.22,0.27,and 0.26,which were,respectively,0.08,0.01,and 0.12 less than those of LSTM.In terms of network structure,ESN consists only of input,reservoir,and output spaces,making it a much simpler model than the others.The proposed ESN was found to be an effective model that,compared to others,converges faster,forecasts more accurately,and builds time-series analyses more easily.
文摘<div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series. </div>
文摘火电机组在现代电力系统中承担着大量的调峰调频任务,通过运行参数建立出力预测模型有助于快速稳定地调整功率。提出一种改进的深度回声状态网络(Deep Echo State Networks,DESN)用于建立机组出力预测模型。该改进型具备可变的记忆能力以应对调整部分运行参数作用于机组出力变化存在的延时性,并根据运行参数聚类生成输入权重进一步挖掘运行参数与出力之间的映射信息。利用华北地区某火电机组不同工作状况下的两种数据集验证了模型效果。结果表明,改进得到的KM-VML-DESN相较于深度回声状态网络、多层感知机、长短期记忆网络等具备更强的预测性能。
文摘针对污水处理过程高度非线性、大滞后等特征,提出了一种基于回声状态网络(echo state network,ESN)模型的多变量自适应预测控制系统.首先,利用ESN建立污水处理过程的智能预测模型,该模型能够预测污水处理的输出;其次,设计污水处理过程的ESN辨识器,将辨识器输出与实际输出的差对主控制器进行误差补偿;最后,以仿真基准模型(BSM1)为平台,采用提出的多变量自适应控制方法对溶解氧浓度和硝态氮浓度进行控制,实验结果表明,该控制方法提高了系统的自适应性和抗干扰能力,能够对溶解氧浓度和硝态氮浓度实现快速、准确跟踪.
