为实现船舶操纵性的在线预报及自适应运动控制,针对Nomoto二阶非线性运动模型参数辨识问题,将最小二乘支持向量机(least squares support vector machines,LSSVM)与多新息方法相结合,提出一种新的多新息在线LSSVM辨识建模方法。试验结...为实现船舶操纵性的在线预报及自适应运动控制,针对Nomoto二阶非线性运动模型参数辨识问题,将最小二乘支持向量机(least squares support vector machines,LSSVM)与多新息方法相结合,提出一种新的多新息在线LSSVM辨识建模方法。试验结果表明,使用所提出的算法辨识的模型进行预报的拟合误差可达到4.76%以下,能准确拟合船舶操纵运动模型。展开更多
For switched linear system with colored measurement noises,the identification difficulties of this system are that there exist unknown switching information,unknown middle variables and noise terms in the information ...For switched linear system with colored measurement noises,the identification difficulties of this system are that there exist unknown switching information,unknown middle variables and noise terms in the information vector.For the mentioned issues,the fuzzy clustering and the multi-innovation recursive identification algorithm are used to deal with these problems.Firstly,the mode detection is transformed into the detection of membership degree values confirmed by the fuzzy clustering method,and the problem of mode detection is solved by judgment and decision of the fuzzy membership values.Moreover,the multi-innovation recursive identification algorithm based on the generalized auxiliary model is proposed to estimate the parameters of the switched linear system with colored noises.Finally,the effectiveness of the proposed method is verified by the results of the simulation example.展开更多
An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnit...An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnitude of fractional order.In this paper,a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently.The nonlinear part is fitted by the neural fuzzy network model,which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models.In addition,the multi-innovation Levenberg-Marquardt(MILM)algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results.A simulation example is given to verify the accuracy and effectiveness of the proposed method.展开更多
This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative(MIGI) approach is proposed to optimize the distance me...This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative(MIGI) approach is proposed to optimize the distance metric of locally weighted learning(LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method’s advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.展开更多
文摘为实现船舶操纵性的在线预报及自适应运动控制,针对Nomoto二阶非线性运动模型参数辨识问题,将最小二乘支持向量机(least squares support vector machines,LSSVM)与多新息方法相结合,提出一种新的多新息在线LSSVM辨识建模方法。试验结果表明,使用所提出的算法辨识的模型进行预报的拟合误差可达到4.76%以下,能准确拟合船舶操纵运动模型。
基金supported by the National Natural Science Foundation of China(61863034)
文摘For switched linear system with colored measurement noises,the identification difficulties of this system are that there exist unknown switching information,unknown middle variables and noise terms in the information vector.For the mentioned issues,the fuzzy clustering and the multi-innovation recursive identification algorithm are used to deal with these problems.Firstly,the mode detection is transformed into the detection of membership degree values confirmed by the fuzzy clustering method,and the problem of mode detection is solved by judgment and decision of the fuzzy membership values.Moreover,the multi-innovation recursive identification algorithm based on the generalized auxiliary model is proposed to estimate the parameters of the switched linear system with colored noises.Finally,the effectiveness of the proposed method is verified by the results of the simulation example.
基金National Natural Science Foundation of China[grant number 61863034].
文摘An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnitude of fractional order.In this paper,a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently.The nonlinear part is fitted by the neural fuzzy network model,which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models.In addition,the multi-innovation Levenberg-Marquardt(MILM)algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results.A simulation example is given to verify the accuracy and effectiveness of the proposed method.
基金financially supported in part by the National High Technology Research and Development Program of China(863Program,Grant No.2015AA016404)the National Natural Science Foundation of China(Grant Nos.51109020,51179019 and 51779029)the Fundamental Research Program for Key Laboratory of the Education Department of Liaoning Province(Grant No.LZ2015006)
文摘This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative(MIGI) approach is proposed to optimize the distance metric of locally weighted learning(LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method’s advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.
