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A reinforcement learning system based on the kernel recursive least-squares algorithm for continuous state-space is proposed in this paper. ... A kernel recursive least-squares-support vector machine is used to realized a mapping from state-action pair to Q-value function. ... In robot-manipulator tracking control problem, we try to train the kernel recursive least square support vector machine so that its outputs can track those of an unknown dynamic system over the time interval ...doi:10.7763/ijmlc.2012.v2.201 fatcat:yiqvowlamrg7jegzq2aegp7p6u
In this paper, we compare the equalization performance of state-space recursive least squares (SSRLS) and state-space recursive least squares with adaptive memory (SSRLSWAM) to offset the effect of a linear ... We consider the equalization of both the linear time invariant and linear time varying systems to compare the performance of these filters. ... Manuscript State-space recursive least-squares (SSRLS) is able to estimate many deterministic signals corrupted by observation noise  . ...doi:10.7763/ijfcc.2013.v2.236 fatcat:rzbat4s4qfczzoiqo2c2yjgdrm
The solution is based on the real-time identification of the physical characteristics of the coupling shaft i.e. stiffness and damping coefficients, by using a physical oriented model and linear recursive ... The efficacy of the suggested condition monitoring system is demonstrated on a prototype transient engine testing facility equipped with a coupling shaft capable of varying its physical properties. ... Recursive Least Squares with Vector Forgetting Factor (RLSVEF) One of the main disadvantages of the standard RLS algorithm is its incapacity to track time-varying system parameters. ...doi:10.1016/j.ymssp.2017.11.023 fatcat:577zjjoa7jfmlgy34kf6ivpzf4
The recursive least squares (RLS) algorithm is well known for tracking dynamic systems. Torres et al. ... Recursive Least Squares The recursive least-squares algorithm is an extension from the least squares (LS). ...doi:10.2514/1.g000408 fatcat:niautfhduzecbfxsviykwx7rje
Then, the moment of inertia matrix parameters can be determined recursively from the system state-space model by matrix transformation. ... The recursive least squares is used to implement the recursive estimation of the state vector, thereby reducing the computation cost of the identification process. ... Then, the timevarying state-space model can be obtained recursively using the adaptive filtering and recursive least squares. ...doi:10.1109/iccis.2017.8274777 dblp:conf/ram/NiLSC17 fatcat:yk6bai27zjdt3ieiepe7miuxmu
A kernel recursive least-squares-support vector machine is used to realize a mapping from state, controller's action and disturber's action to Q-value function. ... A game against nature strategy shows the strength of state importance in terms of accelerated learning, and better relative stability of the system. ... In robot-manipulator tracking control problem, we try to train the kernel recursive least square support vector machine so that its outputs can track those of an unknown dynamic system over the time interval ...doi:10.1016/j.protcy.2016.03.026 fatcat:ntwyiuihprfm7k435fva74xh34
of RLS at the same time by varying forgetting factor according to condition numbers. ... Battery state of health can be monitored by identifying parameters of battery models using various algorithms. ... RECURSIVE LEAST SQUARES Recursive least squares (RLS) is an recursive algorithm for solving the least squares (LS) problem of finding the parameters θ ∈ R n×1 of a linear regression model d t = θ T φ t ...arXiv:1912.02600v4 fatcat:62jxci2ah5henfebumxemsbfey
This QRD-based weighted recursive least-squares (WRLS) algorithm was shown to work well for tracking of static and slowly varying frequency components. ... Combining the state equation with the LP equation in (12) gives the following linear state-space model: (16a) (16b) where is the system state. ...doi:10.1109/tcsii.2007.916837 fatcat:cx5hoc25ffgqvic2ow5wxjjtaa
Then, we vectorize the matricial least squares problem defined for modeling state-space systems such that any method from the equality-constrained least squares framework may be employed. ... Both time-invariant and time-varying cases are considered as well as the case where the state equality constraint is not exactly known. ... However, for time-varying systems, the equality parameter constraint must be enforced at every time instant in the recursive least squares equations (Alenany & Shang, 2013) . ...arXiv:1904.05178v1 fatcat:fuq4qr2d2jfqzbwhqro6hvhltm
Tracking of time- varying phenomena and nonlinear behavior of dynamic system became an important problem in the area of structural system identification. ... Based on the equivalent linear model, the time-variant model parameters can be identified. The tracking ability on the identification of a time-variant system is dis- cussed. ...
recursive least-squares (RLS) algorithm. ... Further, it is shown that the algorithm can accurately track fast time variations in a nonstationary environment. ... ., Department of Electrical Engineering, Michigan State University, East Lansing, for valuable discussions. ...doi:10.1109/78.796429 fatcat:rzz2ztn4ubawlh5nw56o2uwmce
Summary: “The problem of recursive estimation of a state of dynamic systems in the presence of time-varying outliers in obser- vations to be processed is considered. ... Summary: “In signal processing, adaptive filtering algorithms based on recursive least squares minimization are common. ...
As generalization of linear development of classic system identification methods has adaptive filters, the kernel recursive least squares  (KRLS) been ... algorithm for time-varying nonlinear systems: online resizing of the  M. Han, S. Zhang, M. Xu, T. Qiu, and N. ...doi:10.1109/access.2021.3133012 fatcat:6pgdbmctcjfadi2df4xlrxvhje
A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. ... The system is characterized by an impulse response model, which is assumed to lie within a bounded set called the feasible system set. ... The system was described using a state-space model with uncertain parameters which were identified online using a recursive least squares technique. ...doi:10.1016/j.ifacol.2020.12.2467 fatcat:uivlauo2tnailitgvfirghglvi
Recursive Least Mean Square (RLMS) algorithm provides a comprehensive and detailed treatment of the signal model used for beam forming. ... the same time to minimize interference arising from other users by introducing nulls in their directions. ... In The Recursive least mean square algorithm by increasing the number of iterations mean square error is reduced and tracking of desired Signal is improved. ...doi:10.5120/14983-3189 fatcat:jjlwmboenrbubhcua2a3gjdx7y
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