氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一...氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.展开更多
针对回声状态网络(Echo state network, ESN)的结构设计问题,提出基于灵敏度分析的模块化回声状态网络修剪算法(Pruning algorithm for modular echo state network, PMESN).该网络由相互独立的子储备池模块构成.首先利用矩阵的奇异值分...针对回声状态网络(Echo state network, ESN)的结构设计问题,提出基于灵敏度分析的模块化回声状态网络修剪算法(Pruning algorithm for modular echo state network, PMESN).该网络由相互独立的子储备池模块构成.首先利用矩阵的奇异值分解(Singular value decomposition, SVD)构造子储备池模块的权值矩阵,并利用分块对角阵原理生成储备池.然后利用子储备池模块输出和相应的输出层权值向量,定义学习残差对于子储备池模块的灵敏度以及网络规模适应度.利用灵敏度大小判断子储备池模块的贡献度,并根据网络规模适应度确定子储备池模块的个数,删除灵敏度低的子模块.在网络的修剪过程中,不需要缩放权值就可以保证网络的回声状态特性.实验结果说明,所提出的算法有效解决了ESN的网络结构设计问题,基本能够确定与样本数据相匹配的网络规模,具有较好的泛化能力和鲁棒性.展开更多
Purpose-The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle-Torsion Spring(PM-TS)for finger therapy.PM has complex nonlinear dynamics,which makes PM modellin...Purpose-The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle-Torsion Spring(PM-TS)for finger therapy.PM has complex nonlinear dynamics,which makes PM modelling difficult.To realize high-accurate tracking for the robotic hand,an Echo State Network(ESN)-based PID adaptive controller is proposed,even though the plant model is unknown.Design/methodology/approach-To drive a single joint of rehabilitation robotic hand,the paper proposes a new PM-TS actuator comprising a Pneumatic Muscle(PM)and a Torsion Spring(TS).Based on the novel actuator,a wearable robotic hand is designed.By employing the model-free approximation capability of ESN,the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand.An ESN together with Recursive Least Square(RLS)is called a RLSESN,where the ESN output weight matrix is updated by the online RLS learning algorithm.Findings–Practical experiments demonstrate the validity of the PM-TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller.In addition,they also verify the effectiveness of the proposed rehabilitation robotic hand.Originality/value–A new PM-TS actuator configuration that uses a PM and a torsion spring for bi-directional movement of joint is presented.By utilizing the new PM-TS actuator,a novel wearable rehabilitation robotic hand for finger therapy is designed.Based on the unknown plant model,the RLSESN_PID controller is proposed to attain satisfactory performance.展开更多
针对回声状态网络(Echo state network,简记ESN)的不适定问题,提出自适应Tikhonov正则化回声状态网络(Adaptive Tikhonov regularized echo state network,简记ATRESN).ATRESN利用Tikhonov正则化方法代替线性回归学习输出权值,将Tikhono...针对回声状态网络(Echo state network,简记ESN)的不适定问题,提出自适应Tikhonov正则化回声状态网络(Adaptive Tikhonov regularized echo state network,简记ATRESN).ATRESN利用Tikhonov正则化方法代替线性回归学习输出权值,将Tikhonov正则化参数的选择转化为超参数的统计推理问题.仿真结果表明,与其他ESNs相比较,ATRESN能够较好的解决不适定问题,同时具有较高的预测精度和泛化能力.展开更多
研究了具有模型不确定性的机器人操作手轨迹跟踪控制问题.针对以往动态递归神经网络(recurrent neural network,RNN)训练算法难以实现及机械手的强非线性等问题,提出一种基于"增广"策略的回声状态网络(echo state network,ESN...研究了具有模型不确定性的机器人操作手轨迹跟踪控制问题.针对以往动态递归神经网络(recurrent neural network,RNN)训练算法难以实现及机械手的强非线性等问题,提出一种基于"增广"策略的回声状态网络(echo state network,ESN)方法 A-ESN(augmented echo state network),网络使用"增广"学习策略离线训练ESN输出权值,训练过程中加入服从均匀分布的白噪声项来保证动态系统稳定性.针对两关节机械手的轨迹跟踪控制问题,首先用A-ESN辨识机械手不确定部分的逆模型,并用PID反馈控制器补偿A-ESN网络的逆建模误差;然后基于A-ESN设计动态控制器;最后进行了数值仿真,并与常规递归神经网络算法进行了比较,结果显示该方法的控制精度比常规方法有了很大提高,表明了该方法的有效性.展开更多
文摘氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.
文摘针对回声状态网络(Echo state network, ESN)的结构设计问题,提出基于灵敏度分析的模块化回声状态网络修剪算法(Pruning algorithm for modular echo state network, PMESN).该网络由相互独立的子储备池模块构成.首先利用矩阵的奇异值分解(Singular value decomposition, SVD)构造子储备池模块的权值矩阵,并利用分块对角阵原理生成储备池.然后利用子储备池模块输出和相应的输出层权值向量,定义学习残差对于子储备池模块的灵敏度以及网络规模适应度.利用灵敏度大小判断子储备池模块的贡献度,并根据网络规模适应度确定子储备池模块的个数,删除灵敏度低的子模块.在网络的修剪过程中,不需要缩放权值就可以保证网络的回声状态特性.实验结果说明,所提出的算法有效解决了ESN的网络结构设计问题,基本能够确定与样本数据相匹配的网络规模,具有较好的泛化能力和鲁棒性.
基金This work has been supported in part by Hi-tech Research and Development Program of China under Grant 2007AA04Z204 and Grant 2008AA04Z207in part by the Natural Science Foundation of China under Grant 60674105,60975058 and 61075095.
文摘Purpose-The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle-Torsion Spring(PM-TS)for finger therapy.PM has complex nonlinear dynamics,which makes PM modelling difficult.To realize high-accurate tracking for the robotic hand,an Echo State Network(ESN)-based PID adaptive controller is proposed,even though the plant model is unknown.Design/methodology/approach-To drive a single joint of rehabilitation robotic hand,the paper proposes a new PM-TS actuator comprising a Pneumatic Muscle(PM)and a Torsion Spring(TS).Based on the novel actuator,a wearable robotic hand is designed.By employing the model-free approximation capability of ESN,the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand.An ESN together with Recursive Least Square(RLS)is called a RLSESN,where the ESN output weight matrix is updated by the online RLS learning algorithm.Findings–Practical experiments demonstrate the validity of the PM-TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller.In addition,they also verify the effectiveness of the proposed rehabilitation robotic hand.Originality/value–A new PM-TS actuator configuration that uses a PM and a torsion spring for bi-directional movement of joint is presented.By utilizing the new PM-TS actuator,a novel wearable rehabilitation robotic hand for finger therapy is designed.Based on the unknown plant model,the RLSESN_PID controller is proposed to attain satisfactory performance.
文摘针对回声状态网络(Echo state network,简记ESN)的不适定问题,提出自适应Tikhonov正则化回声状态网络(Adaptive Tikhonov regularized echo state network,简记ATRESN).ATRESN利用Tikhonov正则化方法代替线性回归学习输出权值,将Tikhonov正则化参数的选择转化为超参数的统计推理问题.仿真结果表明,与其他ESNs相比较,ATRESN能够较好的解决不适定问题,同时具有较高的预测精度和泛化能力.
文摘研究了具有模型不确定性的机器人操作手轨迹跟踪控制问题.针对以往动态递归神经网络(recurrent neural network,RNN)训练算法难以实现及机械手的强非线性等问题,提出一种基于"增广"策略的回声状态网络(echo state network,ESN)方法 A-ESN(augmented echo state network),网络使用"增广"学习策略离线训练ESN输出权值,训练过程中加入服从均匀分布的白噪声项来保证动态系统稳定性.针对两关节机械手的轨迹跟踪控制问题,首先用A-ESN辨识机械手不确定部分的逆模型,并用PID反馈控制器补偿A-ESN网络的逆建模误差;然后基于A-ESN设计动态控制器;最后进行了数值仿真,并与常规递归神经网络算法进行了比较,结果显示该方法的控制精度比常规方法有了很大提高,表明了该方法的有效性.
