Estimation of time varying signal parameters using an improved Adaline learning algorithm

2014 ◽  
Vol 68 (2) ◽  
pp. 115-129 ◽  
Author(s):  
Sarita Nanda ◽  
Milan Biswal ◽  
P.K. Dash
Keyword(s):  
Learning Algorithm ◽  
Time Varying ◽  
Signal Parameters ◽  
Estimation Of Time

Estimation of time‐varying signal parameters via VFM Kalman filtering

1999 ◽  
Vol 105 (2) ◽  
pp. 1309-1310
Author(s):  
Sang‐Wook Lee ◽  
Jun‐Seok Lim ◽  
Byung‐Doo Jun ◽  
Koeng‐Mo Sung
Keyword(s):  
Kalman Filtering ◽  
Time Varying ◽  
Signal Parameters ◽  
Estimation Of Time

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

2006 ◽  
Vol 129 (3) ◽  
pp. 352-356 ◽  
Author(s):  
Wen Chen ◽  
Mehrdad Saif
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
Fault Estimation ◽  
Time Varying ◽  
Satellite Systems ◽  
Adaptive Observers ◽  
Error Dynamics ◽  
The Stability ◽  
Estimation Of Time ◽  
Identification Strategy

This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform fault estimation instead of using integrators that are commonly used in classical adaptive observers. The stability of estimation-error dynamics is established and proved. An illustrative example clearly shows that time-varying thruster faults can be accurately identified.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

scholarly journals Maximum Likelihood Estimation of Time-Varying Sparsity Level for Dynamic Sparse Signals

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 136687-136701
Author(s):  
Thiruppathirajan S. ◽  
Lakshmi Narayanan R. ◽  
Sreelal S. ◽  
Manoj B. S.
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Sparse Signals ◽  
Time Varying ◽  
Sparsity Level ◽  
Estimation Of Time
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