scholarly journals Distributed Observers for LTI Systems with Finite Convergence Time: A Parameter Estimation-based Approach

2020 ◽  
pp. 1-1
Author(s):  
Romeo Ortega ◽  
Emmanuel Nuno ◽  
Alexey Bobtsov
Keyword(s):  
Parameter Estimation ◽  
Convergence Time ◽  
Finite Convergence

scholarly journals Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Technique ◽  
Convergence Time ◽  
Generation Gap ◽  
Model Order ◽  
Real Coded Genetic Algorithm ◽  
The Mean ◽  
Conventional Genetic Algorithm

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

scholarly journals Markov Chains with finite convergence time

1978 ◽  
Vol 7 (3) ◽  
pp. 247-253 ◽  
Author(s):  
Israel Brosh ◽  
Yigal Gerchak
Keyword(s):  
Markov Chains ◽  
Convergence Time ◽  
Finite Convergence

Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation

2018 ◽  
Vol 7 (3) ◽  
pp. 42-53 ◽  
Author(s):  
Bharat Singh ◽  
Shabana Urooj
Keyword(s):  
Blood Pressure ◽  
Drug Delivery ◽  
Parameter Estimation ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Pressure Control ◽  
Convergence Time ◽  
Therapeutic Drug ◽  
Adaptive Parameter ◽  
Arterial Blood

Controlled drug delivery systems (DDS's) is an electromechanical system that supports the injection of a therapeutic drug intravenously into a patient's body and easily controls the infusion rate of patient's drug, blood pressure, and time of drug release. The controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operations. Different methods have been proposed for controlling MABP, all methods have certain disadvantages according to patient model. In this article, the authors propose blood pressure control using integral reinforcement learning based fuzzy inference systems (IRLFI) based on parameter estimation techniques and have compared this method in terms of integral squared error (ISE), integral absolute error (IAE), integral time-weighed absolute error (ITAE), root mean square error (RMSE), convergence time (CT).

scholarly journals BDS Multipath Parameter Estimation in the Presence of Impulsive Noise

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Jicheng Ding ◽  
Lin Zhao ◽  
Chun Jia ◽  
Zhibin Luo
Keyword(s):  
Parameter Estimation ◽  
Impulsive Noise ◽  
Research Work ◽  
Satellite System ◽  
Performance Assessments ◽  
Convergence Time ◽  
Multipath Mitigation ◽  
Short Delay ◽  
Processing Gain ◽  
Similar Principle

This study focuses on mitigating the multipath, especially the short-delay multipath of the BeiDou navigation satellite system under impulsive noise conditions. A modified least meanp-norm (LMP) algorithm is developed to reduce the convergence time with the same steady-state error by predicting the updating trend of weights. The modified normalized power and the normalized polynomial least meanpth power are also directly provided according to a similar principle. According to the research work, an average filter has been utilized to improve the processing gain of designed mitigation scheme. Some significant simulation results verified the performance of the proposed adaption algorithm. Multipath parameter estimation tests have been conducted under different noise levels. Some comparative statistics performance assessments are quantified and verified under impulsive and additional white Gaussian noise environments. Results with various window widths of the average filtering and carrier-to-noise ratios indicate that the proposed scheme is able to improve the performance of the short-delay multipath mitigation under normal and degraded environments.

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