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Reinforcement Learning with Kernel Recursive Least-Squares Support Vector Machine

Hitesh Shah, M. Gopal
2012 International Journal of Machine Learning and Computing  
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

Comparative Analysis of SSRLS and SSRLS with Adaptive Memory for Wireless Channel Equalization

Muhammad Zeeshan, Ihsan Ullah
2013 International Journal of Future Computer and Communication  
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 [3] .  ... 
doi:10.7763/ijfcc.2013.v2.236 fatcat:rzbat4s4qfczzoiqo2c2yjgdrm

Health monitoring system for transmission shafts based on adaptive parameter identification

I. Souflas, A. Pezouvanis, K.M. Ebrahimi
2018 Mechanical systems and signal processing  
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

Recursive Least Squares for Online Dynamic Identification on Gas Turbine Engines

Zhuo Li, Theoklis Nikolaidis, Devaiah Nalianda
2016 Journal of Guidance Control and Dynamics  
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

On-orbit identification of spacecraft time-varying moment of inertia using an improved recursive subspace method

Zhiyu Ni, Jinguo Liu, Xinhui Shen, Chenguang Chang
2017 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)  
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

Kernel Recursive Least Squares Function Approximation in Game Theory Based Control

Hitesh Shah, M. Gopal
2016 Procedia Technology - Elsevier  
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

Low computational cost method for online parameter identification of Li-ion battery in battery management systems using matrix condition number [article]

Minho Kim, Kwangrae Kim, Soohee Han
2020 arXiv   pre-print
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

A Recursive Frequency Estimator Using Linear Prediction and a Kalman-Filter-Based Iterative Algorithm

Z.G. Zhang, S.C. Chan, K.M. Tsui
2008 IEEE Transactions on Circuits and Systems - II - Express Briefs  
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

Least-Squares Parameter Estimation for State-Space Models with State Equality Constraints [article]

Rodrigo A. Ricco, Bruno O. S. Teixeira
2019 arXiv   pre-print
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

Page 693 of American Society of Civil Engineers. Collected Journals Vol. 126, Issue 7 [page]

2000 American Society of Civil Engineers. Collected Journals  
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.  ... 

BEACON: an adaptive set-membership filtering technique with sparse updates

S. Nagaraj, S. Gollamudi, S. Kapoor, Yih-Fang Huang
1999 IEEE Transactions on Signal Processing  
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

Page 1528 of Mathematical Reviews Vol. , Issue 2003B [page]

2003 Mathematical Reviews  
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.  ... 

A New Sparse Kernel RLS Algorithm for Identification of Nonlinear Systems

Xinyu Guo, Shifeng Ou, Menghua Jiang, Ying Gao, Jindong Xu, Zhuoran Cai
2021 IEEE Access  
As generalization of linear development of classic system identification methods has adaptive filters, the kernel recursive least squares [20] (KRLS) been  ...  algorithm for time-varying nonlinear systems: online resizing of the [22] M. Han, S. Zhang, M. Xu, T. Qiu, and N.  ... 
doi:10.1109/access.2021.3133012 fatcat:6pgdbmctcjfadi2df4xlrxvhje

Robust Adaptive Model Predictive Control with Worst-Case Cost

Anilkumar Parsi, Andrea Iannelli, Mingzhou Yin, Mohammad Khosravi, Roy S. Smith
2020 IFAC-PapersOnLine  
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

A Novel Adaptive Beam forming RLMS Algorithm for Smart Antenna System

M. Kamaraju, K. Ramakrishna, K. Ramanjaneyulu
2014 International Journal of Computer Applications  
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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