Data-driven recursive least squares methods for non-affined nonlinear discrete-time systems

2020 ◽  
Vol 81 ◽  
pp. 787-798 ◽  
Author(s):  
Na Lin ◽  
Ronghu Chi ◽  
Biao Huang
Keyword(s):  
Least Squares ◽  
Discrete Time ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Discrete Time Systems ◽  
Time Systems

scholarly journals Recursive Least-Squares Estimation for the Joint Input-State Estimation of Linear Discrete Time Systems with Unknown Input

2015 ◽  
Vol 5 (2) ◽  
pp. 259 ◽  
Author(s):  
Talel Bessaoudi ◽  
Fayçal Ben Hmida
Keyword(s):  
Least Squares ◽  
State Estimation ◽  
Discrete Time ◽  
Linear Time ◽  
Dynamical Evolution ◽  
Recursive Least Squares ◽  
Input State ◽  
Unknown Input ◽  
Discrete Time Systems ◽  
Time Systems

<p class="Author">This paper presents a recursive least-squares approach to estimate simultaneously the state and the unknown input of linear time varying discrete time systems with unknown input. The method is based on the assumption that no prior knowledge about the dynamical evolution of the input is available. The joint input and state estimation are obtained by recursive least-squares formulation by applying the inversion lemmas. The proposed filter is equivalent to recursive three step filter. To illustrate the performance of the proposed filter an example is given.</p>

scholarly journals Data-Driven Nonzero-Sum Game for Discrete-Time Systems Using Off-Policy Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 14074-14088 ◽  
Author(s):  
Yongliang Yang ◽  
Sen Zhang ◽  
Jie Dong ◽  
Yixin Yin
Keyword(s):  
Reinforcement Learning ◽  
Discrete Time ◽  
Data Driven ◽  
Discrete Time Systems ◽  
Time Systems
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