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Maximum likelihood identification of dual-rate Hammerstein output-error moving average system

IET CONTROL THEORY AND APPLICATIONS 2020年第8期14卷 1078-1090页

作者： Li, Junhong Zhang, Jiali Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This study discusses the parameter estimation of the Hammerstein output-error moving average system using the dual-rate sampled data. The polynomial transformation technique is used to obtain the identification model of the discussed dual-rate sampled systems. The stochastic gradient optimisation method is an effective optimisation method. Compared with the Newton optimisation, it only needs to calculate the first derivative during the optimisation and the amount of calculation is relatively small. It is a good choice to use the stochastic gradient algorithm for the identification of Hammerstein dual-rate model after using the polynomial transformation technique. In order to improve the convergence speed, a maximum likelihood forgetting factor stochastic gradient identification algorithm is proposed by combining the maximum likelihood principle and the gradient search method. The convergence of the algorithm is analysed by using the stochastic process theory. Furthermore, in order to improve the estimation accuracy of the identification algorithm, a maximum likelihood multi-innovation forgetting factor stochastic gradient algorithm is proposed by using the multi-innovation identification theory. The effectiveness of the proposed algorithms is illustrated by a numerical simulation example and a water tank system.

关键词： parameter estimation least squares approximations stochastic processes maximum likelihood estimation optimisation polynomials gradient methods maximum likelihood identification Hammerstein output-error moving average system dual-rate sampled data polynomial transformation technique identification model dual-rate sampled systems stochastic gradient optimisation method Newton optimisation stochastic gradient algorithm Hammerstein dual-rate model maximum likelihood principle gradient search method stochastic process theory water tank system multiple innovation identification theory maximum likelihood multi-innovation forgetting factor stochastic gradient identification algorithm

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Differentially Coherent Multichannel Detection of Acoustic OFDM Signals

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IEEE JOURNAL OF OCEANIC ENGINEERING 2015年第2期40卷 251-268页

作者： Aval, Yashar M. Stojanovic, Milica Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

In this paper, we propose a class of methods for compensating for the Doppler distortions of the underwater acoustic channel for differentially coherent detection of orthogonal frequency-division multiplexing (OFDM) signals. These methods are based on multiple fast Fourier transform (FFT) demodulation, and are implemented as partial (P), shaped (S), fractional (F), and Taylor (T) series expansion FFT demodulation. They replace the conventional FFT demodulation with a few FFTs and a combiner. The input to each FFT is a specific transformation of the input signal, and the combiner performs weighted summation of the FFT outputs. The four methods differ in the choice of the pre-FFT transformation (P, S, F, T), while the rest of the receiver remains identical across these methods. We design an adaptive algorithm of stochastic gradient type to learn the combiner weights for differentially coherent detection. The algorithm is cast into the multichannel framework to take advantage of spatial diversity. The receiver is also equipped with an improved synchronization technique for estimating the dominant Doppler shift and resampling the signal before demodulation. An additional technique of carrier sliding is introduced to aid in the post-FFT combining process when residual Doppler shift is nonnegligible. Synthetic data, as well as experimental data from a recent mobile acoustic communication experiment (few kilometers in shallow water, 10.5-15.5-kHz band) are used to demonstrate the performance of the proposed methods, showing significant improvement over conventional detection techniques with or without intercarrier interference equalization (5-7 dB on average over multiple hours), as well as improved bandwidth efficiency [ability to support up to 2048 quadrature phase-shift keying (QPSK) modulated carriers].

关键词： Differentially coherent detection Doppler fading intercarrier interference (ICI) mitigation orthogonal frequency-division multiplexing (OFDM) stochastic gradient algorithm synchronization underwater acoustic communication

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gradient estimation for sensitivity analysis and adaptive multiuser interference rejection in code-division multiple-access systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 1997年第7期45卷 848-858页

作者： Mandayam, NB Aazhang, B RICE UNIV DEPT ELECT & COMP ENGNHOUSTONTX 77251

In this paper, we consider a direct-sequence code-division multiple-access (DS-CDMA) system in the framework of a discrete-event dynamic system (DEDS) in order to optimize the system performance, Based on this formulation, we develop infinitesimal perturbation analysis (IPA) for estimating the sensitivity of the average probability of bit error to factors ranging from near-far effects to imperfections in power control, The above estimates are shown to be unbiased, and this technique is then further incorporated into a stochastic gradient algorithm for achieving adaptive multiuser interference rejection for such systems, which is also subject to frequency nonselective slow fading, We use an IPA-based stochastic training algorithm for developing an adaptive linear detector with the average probability of error being the minimization criterion, We also develop a practical implementation of such an adaptive detector where we use a joint estimation-detection algorithm for minimizing the average probability of bit error, A sequential implementation that does not require a stochastic training sequence or a preamble is also developed.

关键词： adaptive detection direct-sequence CDMA infinitesimal perturbation analysis stochastic gradient algorithm

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RECURSIVE-IDENTIFICATION AND ADAPTIVE PREDICTION IN LINEAR stochastic-SYSTEMS

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SIAM JOURNAL ON CONTROL AND OPTIMIZATION 1991年第5期29卷 1061-1090页

作者： LAI, TL YING, ZL UNIV ILLINOIS DEPT STATCHAMPAIGNIL 61820

By making use of extended stochastic Lyapunov functions and martingale limit theorems, established herein are certain basic properties of adaptive d-step ahead predictors associated with the stochastic gradient (without interlacing), and monitored recursive maximum likelihood algorithms for recursive identification of an ARMAX system. Both the direct (or implicit) and indirect (or explicit) approaches to adaptive prediction are considered within a unified framework involving stochastic regression models. Applications to adaptive control of ARMAX systems are also discussed.

关键词： ADAPTIVE PREDICTION GLOBAL CONVERGENCE stochastic gradient algorithm AML RECURSIVE MLE stochastic ADAPTIVE CONTROL CERTAINTY EQUIVALENCE ASYMPTOTIC EFFICIENCY

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Time-delay and parameter estimation for an ARX model based on copula theory and Armijo criterion and their applications in the modeling of the dynamics of the UAV

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第17期33卷 10393-10404页

作者： Jing, Shaoxue Chen, Ling Liu, Ranran Huaiyin Normal Univ Sch Phys & Elect Elect Engn 111 Changjiang West Rd Huaian 223300 Jiangsu Peoples R China Jiangsu Univ Technol Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China

Time-delay dynamic systems are widely existed in industrial applications owing to the measure, control or other processes. To carry out system analysis, control and fault diagnosis, the identification of time-delay systems becomes more and more important. This article considers the identification of time-delay ARX models based on a novel two-stage algorithm. Firstly, a 2-copula criterion based time-delay estimation method is presented by using a measure of dependence between the model input and output. This method can obtain the time delay without the estimates of the parameters. Secondly, a multi-gradient algorithm with adaptive stacking length is studied. This algorithm accelerates traditional stochastic gradient algorithm by taking several recent gradients in each iteration. The stacking length, that is, the number of the gradient used in a step, is determined by the Armijo criterion. The proposed algorithm is validated by numerical experiments and the modeling of unmanned aerial vehicle data.

关键词： adaptive stacking length Armijo criterion copula theory multi-gradient parameter estimation stochastic gradient algorithm time-delay estimation

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Adaptive Filtering Under the Maximum Correntropy Criterion With Variable Center

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IEEE ACCESS 2019年 7卷 105902-105908页

作者： Zhu, Lingfei Song, Chengtian Pan, Lizhi Li, Jili Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Huaihai Ind Grp Changzhi 046012 Peoples R China

Recently, an extended version of correntropy, whose center can locate at any position has been proposed and applied in a new optimization criterion called maximum correntropy criterion with variable center (MCC-VC). In order to optimize the performance of adaptive filtering in non-Gaussian and non-zero mean noise environments, in this paper, we propose a stochastic gradient adaptive filtering algorithm for online learning based on MCC-VC and analyze its stability and convergence performance. Moreover, we also extend an online learning approach to estimate the kernel width and the center location, in which two parameters have a great influence on the accuracy of the algorithm. The simulation results of the online learning model have verified the superiority and robustness of the new method.

关键词： Adaptive filtering maximum correntropy criterion with variable center(MCC-VC) stochastic gradient algorithm steady-state excess mean square error

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Performance analysis of the generalised projection identification for time-varying systems

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IET CONTROL THEORY AND APPLICATIONS 2016年第18期10卷 2506-2514页

作者： Ding, Feng Xu, Ling Zhu, Quanmin Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Wuxi Vocat Inst Commerce Sch Internet Things Technol Wuxi 214153 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY Avon England

The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the time-varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.

关键词： time-varying systems least mean squares methods parameter estimation stochastic processes gradient methods performance analysis time-varying systems least mean square methods parameter estimation algorithms stochastic gradient algorithm time-varying parameters generalised projection identification algorithm stochastic process theory generalised projection algorithm forgetting factor recursive least squares algorithm data window length minimum parameter estimation error

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NOVEL STEEPEST DESCENT ADAPTIVE FILTER DERIVED FROM NEW PERFORMANCE-FUNCTION WITH ADDITIONAL EXPONENTIAL TERM

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SIGNAL PROCESSING 1994年第2期36卷 189-199页

作者： JUN, BE PARK, DJ KOREA ADV INST SCI & TECHNOL DEPT ELECT ENGN373-1 KUSONG DONGTAEJONSOUTH KOREA

A new steepest descent adaptive filter algorithm derived from a newly devised performance index function is presented. The performance function of the new algorithm is introduced from that of the least mean square (LMS) considering that the stochastic steepest descent method utilises a gradient search in order to minimise the performance function iteratively. Through mathematical analyses and computer simulations, it is verified that there are significant improvements in convergence speed and misadjustment error. Nevertheless its computational simplicity and robustness are maintained with little degradation (compared to those of the LMS algorithm). The new algorithm can be interpreted as a new kind of variable step size adaptive algorithm, and in this respect a modified method is proposed in order to reduce the noise caused by fluctuation of the varying step size.

关键词： ADAPTIVE FILTERING stochastic gradient algorithm STEEPEST DESCENT METHOD

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Dependence Measure and Wolfe-Powell Criterion Based Two-stage algorithm for Identification of Time Delay FIR Models

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第10期21卷 3484-3491页

作者： Li, Wenhui Jing, Shaoxue Yang, Bin Huaiyin Normal Univ Sch Urban & Environm Sci Huaian 223300 Peoples R China Huaiyin Normal Univ Sch Phys & Elect Elect Engn 111 Changjiang West Rd Huaian 223300 Peoples R China Huaiyin Normal Univ Sch Math Sci Huaian 223300 Peoples R China

Time delay dynamic systems are widely existed due to sensors, actuators or other reasons. In this paper, a time delay FIR system is considered to model linear dynamic systems. The reason why the FIR model is selected is to highlight the proposed time-delay estimation method and parameter identification algorithm, and avoid the impact of a complex model on readers' understanding of the proposed technologies. Firstly, to obtain an estimate of the time delay, a dependence measure based method is proposed. Unlike the optimization method that requires the parameter estimate and needs to round the estimated delay, the delay estimation method based on the 2-copula dependence measure can give accurate delay estimates independently of the parameters and without rounding. Secondly, to estimate the parameters, a variable stacking length multi-gradient identification algorithm is studied. The multi-gradient technique takes recent several gradients to accelerate the stochastic gradient algorithm. The stacking length, i.e., the number of gradients used in each iteration, is determined by the Wolfe-Powell criterion. The effectiveness is tested by numerical simulations and case study.

关键词： Dependence measure multi-innovation parameter estimation stochastic gradient algorithm time delay estimation Wolfe-Powell criterion

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gradient-based simulation optimization under probability constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第2期212卷 345-351页

作者： Andrieu, Laetitia Cohen, Guy Vazquez-Abad, Felisa J. Univ Paris Est CERMICS Ecole Ponts F-77455 Marne La Vallee 2 France EDF R&D Dep OSIRIS F-92141 Clamart France CUNY Hunter Coll Dept Comp Sci New York NY 10065 USA

We study optimization problems subject to possible fatal failures. The probability of failure should not exceed a given confidence level. The distribution of the failure event is assumed unknown, but it can be generated via simulation or observation of historical data. gradient-based simulation-optimization methods pose the difficulty of the estimation of the gradient of the probability constraint under no knowledge of the distribution. In this work we provide two single-path estimators with bias: a convolution method and a finite difference, and we provide a full analysis of convergence of the Arrow-Hurwicz algorithm, which we use as our solver for optimization. Convergence results are used to tune the parameters of the numerical algorithms in order to achieve best convergence rates, and numerical results are included via an example of application in finance. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Probability constraints stochastic gradient algorithm stochastic approximation

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