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Maximum likelihood based identification methods for rational models

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2019年第14期50卷 2579-2591页

作者： Chen, Jing Ding, Feng Zhu, Quanmin Liu, Yanjun Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao Shandong Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China Univ West England Dept Engn Design & Math Bristol Avon England

In a rational model, some terms of the information vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome this shortcoming, this paper develops two recursive algorithms for estimating the rational model parameters. These two algorithms, based on the maximum likelihood principle, have three integrated key features: (1) to establish two unbiased maximum likelihood recursive algorithms, (2) to develop a maximum likelihood recursive least squares (ML-RLS) algorithm to decrease the computational efforts, (3) to update the parameter estimates by the ML-RLS based particle swarm optimisation (ML-RLS-PSO) algorithm when the noise-to-output ratio is large. Comparative studies demonstrate that (1) the ML-RLS algorithm is only valid for rational models when the noise-to-output ratio is small, (2) the ML-RLS-PSO algorithm is effective for rational models with random noise-to-output ratio, but at the cost of heavy computational efforts. Furthermore, the simulations provide cases for potential expansion and applications of the proposed algorithms.

关键词： Parameter estimation rational model maximum likelihood method PSO algorithm recursive least squares algorithm

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Fast robust quasi-Newton algorithm for adaptive arrays

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IEE PROCEEDINGS-COMMUNICATIONS 1999年第4期146卷 231-239页

作者： Klemes, M Nortel Networks Dept R412 Microwave Radio Syst Dev Ottawa ON K1Y 4H7 Canada

A stable, fast (order-of-N) quasi-Newton (QN) algorithm (FRQN) applicable to arbitrary at-ray signals is presented. Its complexity is only twice that of the normalised least mean squares (NLMS) algorithm, yet its convergence is faster and smoother, proceeding along the optimal QN path as that of the order N-2 recursive least squares (RLS) algorithm, albeit more slowly. The development of its recursive update equation is outlined and compared to those of the NLMS and RLS algorithms, as well as variations of the conjugate gradient (CG) algorithm. Analytic expressions for its excess mean squared error in stationary and non-stationary scenarios are presented and compared with those of the LMS algorithm. Simulations of an adaptive array in a mobile wireless base-station show that the FRQN performance falls between that of the NLMS and RLS algorithms for a wide range of signal scenarios.

关键词： mean square error methods stable quasi-Newton algorithm normalised least mean squares adaptive signal processing computational complexity Antenna arrays radiofrequency interference mean squared error mobile wireless base-station array signals Signal processing and detection convergence recursive least squares algorithm recursive update equation Mobile radio systems nonstationary signals Electromagnetic compatibility and interference Newton method numerical stability complexity conjugate gradient algorithm NLMS algorithm interference cancellation land mobile radio Interpolation and function approximation (numerical analysis) adaptive antenna arrays digital adaptive array algorithm stationary signals fast robust quasi-Newton algorithm RLS algorithm interference suppression

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Complex RLS fuzzy adaptive decision feedback equalizer

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IEICE TRANSACTIONS ON COMMUNICATIONS 1996年第12期E79B卷 1911-1913页

作者： Lee, SY Kim, JB Lee, CJ Lee, KY Lee, CW CHANGWON NATL UNIV DEPT ELECT ENGN CHANG WON SOUTH KOREA DONGYANG TECH COLL DEPT ELECT & COMM ENGN SEOUL SOUTH KOREA

A complex fuzzy adaptive decision feedback equalizer based on the RLS algorithm is proposed. The proposed equalizer not only improves the performance but also reduces the computational complexity compared with the conventional complex fuzzy adaptive equalizers under the assumption of perfect knowledge of the linear and nonlinear channels.

关键词： fuzzy set decision feedback equalizer recursive least squares algorithm

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DOA/MoM-based ABF algorithm for SINR enhancement

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IET COMMUNICATIONS 2019年第11期13卷 1565-1572页

作者： Dawood, Heba S. Hussein, Amr Hussein Gemeay, Entesar Attia, Mahmoud Ahmed Tanta Univ Fac Engn Elect & Elect Commun Dept Tanta Egypt

In this study, a new non-iterative adaptive beamforming (ABF) algorithm for the signal-to-interference and noise ratio (SINR) enhancement is proposed. It is based on a combination between the direction of arrival (DOA) estimation and the method of moments (MoM). The proposed algorithm is denoted as DM/ABF which stands for DOA and MoM-based ABF. The DOA is used to provide accurate estimates for the directions of the desired and interfering signals. On the basis of the estimated DOAs, a dedicated shaped pattern version of the ordinary pattern is created and applied as the desired input to the MoM algorithm. The MoM is used for shaped pattern synthesis to estimate the weights vector required to provide deep nulls toward the interfering signals and directs the main beam toward the desired signal. In this case, the weights vector does not update iteratively at each received signal sample as in case of least mean square (LMS) and recursive least squares (RLSs) algorithms, but it is updated only when the estimated DOAs of the desired and interfering signals are changed. Furthermore, a large number of close nulls can be produced without the need for additional antenna elements compared with other algorithms.

关键词： direction-of-arrival estimation array signal processing method of moments least mean squares methods adaptive antenna arrays antenna radiation patterns interference (signal) SINR enhancement smart antenna systems adaptive beamforming array pattern desired direction interfering signals noniterative ABF algorithm arrival estimation desired signals dedicated shaped pattern version ordinary pattern desired input MoM algorithm shaped pattern synthesis weights vector desired signal received signal sample direction-of-arrival MoM-based ABF algorithm signal-to-interference-and-noise ratio enhancement method-of-moments least mean square algorithm recursive least squares algorithm LMS algorithm RLS algorithm

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Multi-branch successive interference cancellation for MIMO spatial multiplexing systems: design, analysis and adaptive implementation

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IET COMMUNICATIONS 2011年第4期5卷 484-494页

作者： Fa, R. de Lamare, R. C. Univ York Dept Elect Commun Res Grp York YO10 5DD N Yorkshire England

In this study, the authors propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output spatial multiplexing systems based on a structure with multiple interference cancellation branches. The proposed multi-branch SIC (MB-SIC) structure employs multiple SIC schemes in parallel and each branch detects the signal according to its respective ordering pattern. By selecting the branch which yields the estimates with the best performance according to the selection rule, the MB-SIC detector, therefore, achieves higher detection diversity. The authors consider three selection rules for the proposed detector, namely, the maximum likelihood (ML), the minimum mean square error and the constant modulus criteria. An efficient adaptive receiver is developed to update the filter weight vectors and estimate the channel using the recursive least squares algorithm. Furthermore a bit error probability performance analysis is carried out. The simulation results reveal that the authors' scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML and sphere decoder detectors.

关键词： bit error probability performance analysis multibranch SIC structure Electromagnetic compatibility and interference Communication switching maximum likelihood space division multiplexing multiple-input multiple-output spatial multiplexing systems error statistics least mean squares methods minimum mean square error Other topics in statistics higher detection diversity diversity reception channel estimation error propagation radio receivers adaptive receiver multibranch successive interference cancellation constant modulus criteria Interpolation and function approximation (numerical analysis) recursive least squares algorithm MIMO communication Radio links and equipment Communication channel equalisation and identification selection rule radiofrequency interference MB-SIC detector interference suppression

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Energy Consumption Prediction and Optimization of Electric Vehicles Based on RLS and Improved SOA

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IEEE ACCESS 2024年 12卷 38180-38191页

作者： Liu, Chunling Changchun Univ Technol Inst Automot Engn Coll Humanities & Informat Changchun 130000 Peoples R China

The new energy vehicle industry is facing new challenges. To predict and optimize the energy consumption of electric vehicles, this study predicts energy consumption based on the energy consumption characteristics of the electric vehicle power system and air conditioning system, and combines path optimization algorithms for energy-saving path planning. The study first improves the recursive least squares algorithm by combining the forgetting factor, and constructs a vehicle energy consumption identification model based on the improved recursive least squares algorithm and neural network. Then, a path optimization model based on improved seagull optimization is established using chaotic mapping strategy and t-distribution to improve the seagull optimization algorithm. The results showed that the predicted final energy consumption of the model constructed in the study was 2.81kW.h, with an error rate of 5.1%. The improved seagull optimization algorithm obtained an optimal solution of 30.88m for burma14 and 423.74m for oliver30, which were consistent with the published optimal solutions. When the air conditioning was turned on, the energy consumption of the path selected by the algorithm was reduced by about 5.6%. Under the condition of not turning on the air conditioning, the energy consumption of the path selected by the algorithm was reduced by about 4.98%. In summary, the model constructed through research has good application effects in predicting and optimizing vehicle energy consumption. The contribution of the research lies in it helps to reveal the laws of energy utilization in electric vehicles, improve the economy, safety, and environmental friendliness of electric vehicles during operation, and promote the overall management of new energy vehicles.

关键词： recursive least squares algorithm seagull optimization algorithm vehicle energy consumption neural network path planning

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On steady-state performance of the fixed-point RLS algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 1999年第1期25卷 1-16页

作者： Adali, T Ardalan, SH Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA XCAD Corp Cary NC 27511 USA

This paper presents an analysis of a fixed-point recursive least squares (RLS) algorithm for first-order Markov channel estimation and derives expressions for the mean weight misadjustment. The expressions derived are general in that they take into account the correlation in the input. It is shown that correlation amplifies the effect of roundoff error due to the desired signal estimate computation and the additive system noise. The misadjustment due to time-varying system weights and the weight update roundoff error behave similarly and are minimally affected by the input correlation. They contribute to the total misadjustment in such away that is directly proportional to the algorithm's time constant which is a function of the algorithm forgetting factor;The contributions of system noise and roundoff error due to the desired estimate, on the other hand, are inversely proportional to the algorithm time constant. Hence, they indicate a tradeoff in the choice of the forgetting factor to balance the effects of these noise sources. We present simulation results which demonstrate very good agreement with the theory, (C) 1999 Elsevier Science Ltd, All rights reserved.

关键词： fixed-point analysis RLS algorithm system identification roundoff errors time-varying systems steady-state performance recursive least squares algorithm input correlation effects

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An indirect adaptive pole-placement control for MIMO discrete-time stochastic systems

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2005年第7期19卷 547-573页

作者： Yu, WS Tatung Univ Dept Elect Engn Taipei 10451 Taiwan

In this paper, an indirect adaptive pole-placement control scheme for multi-input multi-output (MIMO) discrete-time stochastic systems is developed. This control scheme combines a recursive least squares (RLS) estimation algorithm with pole-placement control design to produce a control law with self-tuning capability. A parametric model with a priori prediction outputs is adopted for modelling the controlled system. Then, a RLS estimation algorithm which applies the a posteriori prediction errors is employed to identify the parameters of the model. It is shown that the implementation of the estimation algorithm including a time-varying inverse logarithm step size mechanism has an almost sure convergence. Further, an equivalent stochastic closed-loop system is used here for constructing near supermartingales, allowing that the proposed control scheme facilitates the establishment of the adaptive pole-placement control and prevents the closed-loop control system from occurring unstable pole-zero cancellation. An analysis is provided that this control scheme guarantees parameter estimation convergence and system stability in the mean squares sense almost surely. Simulation studies are also presented to validate the theoretical findings. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： indirect adaptive pole-placement control MIMO stochastic systems recursive least squares algorithm near supermartingales non-holonomic mobile robot

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L₁-Regularized STAP algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第2期60卷 674-686页

作者： Yang, Zhaocheng de Lamare, Rodrigo C. Li, Xiang Natl Univ Def Technol Res Inst Space Elect Elect Sci & Engn Sch Changsha 410073 Hunan Peoples R China Univ York Dept Elect Commun Res Grp York YO10 5DD N Yorkshire England

In this paper, we propose novel l(1)-regularized space-time adaptive processing (STAP) algorithms with a generalized sidelobe canceler architecture for airborne radar applications. The proposed methods suppose that a number of samples at the output of the blocking process are not needed for sidelobe canceling, which leads to the sparsity of the STAP filter weight vector. The core idea is to impose a sparse regularization (l(1)-norm type) to the minimum variance criterion. By solving this optimization problem, an l(1)-regularized recursive least squares (l(1)-based RLS) adaptive algorithm is developed. We also discuss the SINR steady-state performance and the penalty parameter setting of the proposed algorithm. To adaptively set the penalty parameter, two switched schemes are proposed for l(1)-based RLS algorithms. The computational complexity analysis shows that the proposed algorithms have the same complexity level as the conventional RLS algorithm (O((NM)(2))), where NM is the filter weight vector length), but a significantly lower complexity level than the loaded sample covariance matrix inversion algorithm (O((NM)(3))) and the compressive sensing STAP algorithm (O((NsNd)(3)), where NsNd > NM is the angle-Doppler plane size). The simulation results show that the proposed STAP algorithms converge rapidly and provide a SINR improvement using a small number of snapshots.

关键词： Airborne radar generalized sidelobe canceler architecture L-1-regularized recursive least squares algorithm space-time adaptive processing sparsity switched schemes

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Incremental Learning Framework for Autonomous Robots Based on Q-Learning and the Adaptive Kernel Linear Model

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2022年第1期14卷 64-74页

作者： Hu, Yanming Li, Decai He, Yuqing Han, Jianda Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300071 Peoples R China

The performance of autonomous robots in varying environments needs to be improved. For such incremental improvement, here we propose an incremental learning framework based on Q-learning and the adaptive kernel linear (AKL) model. The AKL model is used for storing behavioral policies that are learned by Q-learning. Both the structure and parameters of the AKL model can be trained using a novel L2-norm kernel recursive least squares (L2-KRLS) algorithm. The AKL model initially without nodes and gradually accumulates content. The proposed framework allows to learn new behaviors without forgetting the previous ones. A novel local epsilon-greedy policy is proposed to speed the convergence rate of Q-learning. It calculates the exploration probability of each state for generating and selecting more important training samples. The performance of our incremental learning framework was validated in two experiments. A curve-fitting example shows that the L2-KRLS-based AKL model is suitable for incremental learning. The second experiment is based on robot learning tasks. The results show that our framework can incrementally learn behaviors in varying environments. Local epsilon-greedy policy-based Q-learning is faster than the existing Q-learning algorithms.

关键词： Autonomous robots incremental learning L2-norm path planning Q-learning recursive least squares algorithm

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