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A novel maximum likelihood-based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2019年第1期32卷 23-29页

作者： Pu, Yan Chen, Jing Jiangnan Univ Sch Sci Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China

This paper proposes a novel maximum likelihood based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise. The unknown noises in the information vector are replaced by their estimates, and then the parameters can be obtained by using the proposed algorithm through the noise estimates. Compared with the maximum likelihood-based recursive least squares algorithm, the proposed algorithm has less computation burden. Furthermore, the performance of the proposed algorithm is analysed and compared using a simulation example.

关键词： system identification stochastic gradient algorithm recursive least squares algorithm maximum likelihood Hammerstein system

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Fractional order PID controller with online tuning based on FRIT and RLS

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2017年第2期27卷 125-135页

作者： Dif, Ahlem Djouambi, Abdelbaki Charef, Abdelfatah Voda, Alina Univ Oum El Bouaghi Dept Sci & Appl Sci Lab Elect Engn & Automat LGEA Oum El Bouaghi 04000 Algeria Univ Constantine 1 Dept Elect Route Ain El Bey Constantine 25011 Algeria Univ J Fourier Gipsa Lab Dept Automat 961 Rue Houille BlancheBP 46 F-38402 St Martin Dheres France

This paper presents a new strategy for online robust control-based fractional order PID controller (FPID). The technique suggests an online parameters tuning of the FPID using fictitious reference iterative tuning (FRIT) approach. This approach enables us to track the desired optimal FPID parameters online using experimental data without any plant model identification. After having set the optimal non-integer orders a and a, the gains of the FPID are then estimated in a recursive manner using the so-called recursive least squares (RLSs) algorithm with forgetting factor, which can cope with variation of plant characteristics adaptively. The performances of this technique are compared with other results already existing in the literature through some typical examples;the results of the proposed approach show significant performance improvement.

关键词： fractional order PID control fictitious reference iterative tuning FRIT recursive least squares algorithm online tuning

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SEQUENTIAL AND PARALLEL SCHEMES FOR ADAPTIVE 2-D PARAMETER-ESTIMATION WITH APPLICATION TO IMAGE ESTIMATION

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 1990年第3期15卷 213-234页

作者： DESAI, UB DITTIA, ZD KUMAR, PS MUNDKUR, PY Department of Electrical Engineering Indian Institute of Technology Powai Bombay India

In this paper, the non-causal quarter plane 2-D recursive least squares (2D-RLS) algorithm for adaptive processing is developed. The complexity of this algorithm turns out to be O(L6) per iteration, for an L x L window. With the aim of reducing this complexity, the matrix gains appearing in the algorithm are replaced by scalar gains. This approach yields the Approximate 2-D recursive least squares (A2D-RLS) algorithm, which is shown to have a complexity of O(L2). With the objective of reducing the computation time even further, a parallel scheme is developed for the A2D-RLS algorithm. Since the algorithm is inherently sequential, its parallelization involves some more approximations. The desired accuracy of the estimated parameters is shown to place an upper bound on the number of processors. The parallel scheme is suitable for implementation on shared memory as well as distributed memory machines. The algorithm is applied to the problem of image estimation. Simulation results giving speed-up, efficiency, and the accuracy of the estimated image are presented.

关键词： ADAPTIVE 2-D PARAMETER ESTIMATION IMAGE ESTIMATION ADAPTIVE PROCESSING recursive least squares algorithm

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A new multi-output neural model with tunable activation function and its applications

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NEURAL PROCESSING LETTERS 2004年第2期20卷 85-104页

作者： Shen, YJ Wang, BW Chen, FX Cheng, L Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Peoples R China Lehigh Univ Dept Comp Engn & Sci Bethlehem PA 18015 USA

In this paper, a new multi-output neural model with tunable activation function (TAF) and its general form are presented. It combines both traditional neural model and TAF neural model. recursive least squares algorithm is used to train a multilayer feedforward neural network with the new multi-output neural model with tunable activation function (MO-TAF). Simulation results show that the MO-TAF-enabled multi-layer feedforward neural network has better capability and performance than the traditional multilayer feedforward neural network and the feedforward neural network with tunable activation functions. In fact, it significantly simplifies the neural network architecture, improves its accuracy and speeds up the convergence rate.

关键词： multi-output neural model neural network recursive least squares algorithm tunable activation function

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Research of discrete wavelet transform domain adaptive equalization algorithm

Research of discrete wavelet transform domain adaptive equal...

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8th International Conference on Signal Processing

作者： Niu, Xueyu PLA Univ Sci & Tech Inst Commun Engn Nanjing 210007 Peoples R China

ISBN: (纸本)9780780397361

This paper studies an algorithm of discrete wavelet transform domain adaptive equalization. At first, the received signals through the channel are transformed in wavelet domain, then the least Mean squares (LMS) algorithm is used to complete the linear equalization in the same domain. The simulation results show its that the wavelet transform domain adaptive equalization algorithm may offer better performance and higher convergence rate than the standard LMS linear algorithm.

关键词： inter-symbol interference discrete wavelet transform least Mean squares algorithm recursive least squares algorithm adaptive equalization

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Polynomial Nonlinear Fractional State Space System Identification 19

Polynomial Nonlinear Fractional State Space System Identific...

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19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

作者： Marzougui, Soumaya Atitallah, Asma Bedoui, Saida Abderrahim, Kamel Univ Gabes Natl Sch Engn Gabes Res Lab Numer Control Ind Proc Gabes Tunisia

ISBN: (纸本)9781728112923

This paper deals with the problem of identification of fractional Hammerstein systems. This model consists of a static nonlinear block in series with a linear fractional-order state space system. This problem poses different difficulties, because, it consists of estimating the system parameters, the states and the fractional order. To simplify this problem, we assume in this paper that the fractional order is known, then, a new identification algorithm is performed: firstly, the parameters of both the linear and the nonlinear subsystems are estimated based on the least squares algorithm, then, the states are updated based on the auxiliary model principle using the estimated parameters. Finally, a numerical simulation is realized in the extent to evaluate the performance of the presented method.

关键词： Fractional Hammerstein system Identification Parameter estimation State estimation recursive least squares algorithm Auxiliary model

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A Variable Forgetting Factor QRD-based RLS algorithm with Bias Compensation for System Identification with Input Noise

A Variable Forgetting Factor QRD-based RLS Algorithm with Bi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tan, H. J. Chan, S. C. Zhang, L. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Rd Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479953417

This paper proposes a variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QR-RLS-BC) for system identification with input noise. The new algorithm is based on the least square estimation with bias compensation framework and it employs a variable forgetting factor to improve the tracking speed and a QRD-based implementation for recursively solving the LS problem with bias compensation. Simulation results show that the proposed method can obtain improved convergence rate in sudden system change environment and satisfactory performance under stationary environment.

关键词： System identification in input noise bias-compensation recursive least squares algorithm variable forgetting factor QR decomposition

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System Identification of Hydraulic System Using RLS algorithm for Feedforward Position Control 12

System Identification of Hydraulic System Using RLS Algorith...

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12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Lee, Kyeong Ha Baek, Seung Guk Choi, Jong Yoon Park, Jae Jun Choi, Hyouk Ryeol Moon, Hyungpil Koo, Ja Choon Sungkyunkwan Univ Sch Mech Engn Suwon 151742 South Korea KNR Syst Inc Adv Res Team Yongin 449881 South Korea

ISBN: (纸本)9781467379717

In this paper, system identification algorithm of an electro-hydraulic system for feedforward position control method is proposed. Feedforward controller using inverse model of feedback system generates filtered signals that compensate the magnitude and phase. In this manner, the feedback system has been properly identified. We estimated the system as linear discrete time transfer function by recursive least squares algorithm. The real-time experiment is conducted by NI-CompactRio and Labview, and MATLAB/Simulink for the validation of suggested method.

关键词： Hydraulic actuator recursive least squares algorithm System identification

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Gear Fault Diagnosis Method Based On Volterra Kernel Identification

Gear Fault Diagnosis Method Based On Volterra Kernel Identif...

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Chinese Automation Congress (CAC)

作者： Yu Guanming Chen Yongqi Zhao Yiming Chen Yang Ningbo Univ Coll Sci & Technol Ningbo 315211 Zhejiang Peoples R China

ISBN: (纸本)9781467371896

For gear fault classification problem, most of fault diagnosis methods only use the sensor output signal to diagnose. For this reason, they can not reflect the system as a whole transmission characteristic and so inevitably there are limitations. a fault diagnosis method based on Volterra kernel, which uses input and output signal in gear system, is presented. The method uses the first order, second order and third order time domain kernel, which is solved by recursive least squares algorithm, to determine the current fault status of the gear. The experimental results show that the method can judge accurately the gear fault from the characteristics of time domain kernel.

关键词： Gear fault diagnosis Volterra series recursive least squares algorithm

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Nonlinear Stochastic Time-varying System Identification Based on Multi-dimensional Taylor Network with Optimal Structure 16

Nonlinear Stochastic Time-varying System Identification Base...

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16th International Conference on Control, Automation and Systems (ICCAS)

作者： Zhang, Chao Yan, Hong-Sen Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ MOE Key Lab Measurement & Control Complex Syst En Nanjing 210096 Jiangsu Peoples R China Henan Inst Technol Dept Automat Control Xinxiang 453003 Peoples R China

ISBN: (纸本)9788993215120

A novel method for nonlinear stochastic time-varying systems identification based on multi-dimensional Taylor network with optimal structure is proposed. In this paper, the connection weight coefficients of multi-dimensional Taylor network are regarded as the time-varying parameters, which are trained by the variable forgetting factor recursive least squares algorithm, to reflect the input-output change of nonlinear stochastic time-varying systems. Moreover, to avoid the dimension explosion, the weight-elimination algorithm is introduced to choose effective regression items of multi-dimensional Taylor network, thereby the simplest structure of network which has the best generalization ability is obtained. Simulation results show that the method proposed in this paper is valid.

关键词： Multi-dimensional Taylor Network stochastic time-varying nonlinear systems weight-elimination algorithm variable forgetting factor recursive least squares algorithm

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