scholarly journals Parameter Estimation of a Class of Neural Systems with Limit Cycles

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 169
Author(s):  
Xuyang Lou ◽  
Xu Cai ◽  
Baotong Cui
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Limit Cycles ◽  
Neural Model ◽  
Recursive Least Squares ◽  
Neural Systems ◽  
Parameter Vector ◽  
Gradient Algorithms ◽  
Simulation Results ◽  
Identification Model

This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their multi-innovation versions by introducing an innovation vector are proposed. The simulation results of the FitzHugh–Nagumo model indicate that the proposed algorithms perform according to the expected effectiveness.

Parameter Estimation for Wiener Systems with Preload Nonlinearities

2014 ◽  
Vol 989-994 ◽  
pp. 1460-1463
Author(s):  
Yun Xia Ni ◽  
Jian Dong Cao
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Recursive Least Squares ◽  
Switching Function ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Recursive Least Squares Algorithm ◽  
Wiener Systems ◽  
Least Squares Algorithm ◽  
Identification Model

This paper proposes a recursive least squares algorithm for Wiener systems. We use a switching function to turn the modelof the nonlinear Wiener systems into an identification model, then propose a recursive least squares identification algorithm toestimate all the unknown parameters of the systems. Finally, an example is provided to show the effectiveness of the proposed algorithm.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

SYSTOLIC ALGORITHMS FOR RECURSIVE TOTAL LEAST SQUARES PARAMETER ESTIMATION AND MIXED RLS/RTLS PROBLEMS

1993 ◽  
Vol 04 (01) ◽  
pp. 55-68 ◽  
Author(s):  
MARC MOONEN
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Total Least Squares ◽  
Parallel Architectures ◽  
Least Squares Estimation ◽  
Recursive Least Squares ◽  
Recursive Algorithms ◽  
Least Squares Problems ◽  
Linearly Constrained ◽  
Systolic Algorithms

Total least squares parameter estimation is an alternative to least squares estimation though much less used in practice, partly due to the absence of efficient recursive algorithms or parallel architectures. Here it is shown how previously developed systolic algorithms/architectures for recursive least squares estimation can be used for recursive total least squares problems. Unconstrained as well as linearly constrained and "mixed RLS/RTLS" problems are considered.

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