Convergence analysis and a synchronized learning algorithm for a joint lattice predictor and FIR adaptive filter

2003 ◽  
Vol 87 (2) ◽  
pp. 67-78
Author(s):  
Naoki Tokui ◽  
Kenji Nakayama ◽  
Akihiro Hirano
Keyword(s):  
Convergence Analysis ◽  
Adaptive Filter ◽  
Learning Algorithm

scholarly journals Tuning heuristics and convergence analysis of reinforcement learning algorithm for online data-based optimal control design

2020 ◽  
Vol 9 (2) ◽  
pp. e188922128
Author(s):  
Fábio Nogueira da Silva ◽  
João Viana Fonseca Neto
Keyword(s):  
Reinforcement Learning ◽  
Convergence Analysis ◽  
Output Feedback ◽  
Learning Algorithm ◽  
Policy Iteration ◽  
Recursive Least Squares ◽  
Input Output ◽  
Output Data ◽  
Data Generator ◽  
Reinforcement Learning Algorithm

A heuristic for tuning and convergence analysis of the reinforcement learning algorithm for control with output feedback with only input / output data generated by a model is presented. To promote convergence analysis, it is necessary to perform the parameter adjustment in the algorithms used for data generation, and iteratively solve the control problem. A heuristic is proposed to adjust the data generator parameters creating surfaces to assist in the convergence and robustness analysis process of the optimal online control methodology. The algorithm tested is the discrete linear quadratic regulator (DLQR) with output feedback, based on reinforcement learning algorithms through temporal difference learning in the policy iteration scheme to determine the optimal policy using input / output data only. In the policy iteration algorithm, recursive least squares (RLS) is used to estimate online parameters associated with output feedback DLQR. After applying the proposed tuning heuristics, the influence of the parameters could be clearly seen, and the convergence analysis facilitated.

scholarly journals Convergence Analysis of LMS based Adaptive filter

2010 ◽  
Author(s):  
Amrita Rai ◽  
Amit Kumar Kohli ◽  
R. B. Patel ◽  
B. P. Singh
Keyword(s):  
Convergence Analysis ◽  
Adaptive Filter

scholarly journals Artefacts Removal from ECG Signal: Dragonfly Optimization-based Learning Algorithm for Neural Network-enhanced Adaptive Filtering

2020 ◽  
Vol 21 (2) ◽  
pp. 247-263
Author(s):  
Talabattula Viswanadham ◽  
Rajesh Kumar P
Keyword(s):  
Neural Network ◽  
Adaptive Filter ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Ecg Signal ◽  
Minimum Error ◽  
Ecg Signals ◽  
Subtraction Procedure ◽  
Primary Signal ◽  
Effective Diagnosis

Electrocardiogram (ECG) artefact removal is the major research topic as the pure ECG signals are an essential part of diagnosing heart-related problems. ECG signals are highly prominent to the interaction with the other signals like the Electromyography (EMG), Electroencephalography (EEG), and Electrooculography (EOG) signals and the interference mainly occurs at the time of recording. The removal of the artefacts from the ECG signal is a hectic challenge, for which, a novel algorithm is proposed in this work. The proposed method utilizes the adaptive filter termed as the (Dragonfly optimization + Levenberg Marqueret learning algorithm) DLM-based Nonlinear Autoregressive with eXogenous input (NARX) neural network for the removal of the artefacts from the ECG signals. Once the artefact signal is identified using the adaptive filter, the identified signal is subtracted from the primary signal that is composed of the ECG signal and the artefacts through an adaptive subtraction procedure. The clean signal thus obtained is used for effective diagnosis purposes, and the experimentation performed to prove the effectiveness of the proposed method proves that the proposed method obtained a maximum Signal-to-noise ratio (SNR) of 52.8789 dB, a minimum error of 0.1832, and minimum error of 0.428.

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