Recursive least squares identification of heat exchanger system using block-structured models

2021 ◽  
pp. 095965182110549
Author(s):  
Saurav Gupta ◽  
Subhransu Padhee ◽  
Libor Pekar
Keyword(s):  
Heat Exchanger ◽  
Least Squares ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Saturated Steam ◽  
Hammerstein Model ◽  
Recursive Identification ◽  
Identification Scheme ◽  
Block Structured

This study provides a recursive parametric identification scheme for a liquid-saturated steam heat exchanger system. The recursive identification scheme uses block-structured Wiener and Hammerstein models as model structure and recursive least squares estimation scheme as the parameter estimation method. The estimated block-oriented model provides higher accuracy of estimation than linear models provided in the literature. From the simulation results, it is observed that the Wiener model can provide 88% goodness-of-FIT, whereas Hammerstein model can provide 96% goodness-of-FIT using the said technique.

scholarly journals Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Qibing Jin ◽  
Youliang Ye ◽  
Wu Cai ◽  
Zeyu Wang
Keyword(s):  
Local Search ◽  
Fractional Order ◽  
Fitness Function ◽  
Descent Method ◽  
Steepest Descent ◽  
Recursive Least Squares ◽  
Steepest Descent Method ◽  
Hammerstein Model ◽  
Recursive Identification ◽  
Initial Value

This paper deals with the identification of the fractional order Hammerstein model by using proposed adaptive differential evolution with the Local search strategy (ADELS) algorithm with the steepest descent method and the overparameterization based auxiliary model recursive least squares (OAMRLS) algorithm. The parameters of the static nonlinear block and the dynamic linear block of the model are all unknown, including the fractional order. The initial value of the parameter is obtained by the proposed ADELS algorithm. The main innovation of ADELS is to adaptively generate the next generation based on the fitness function value within the population through scoring rules and introduce Chebyshev mapping into the newly generated population for local search. Based on the steepest descent method, the fractional order identification using initial values is derived. The remaining parameters are derived through the OAMRLS algorithm. With the initial value obtained by ADELS, the identification result of the algorithm is more accurate. The simulation results illustrate the significance of the proposed algorithm.

scholarly journals A Least Squares Estimation Method for the Linear Learning Model

1978 ◽  
Vol 15 (1) ◽  
pp. 145-153
Author(s):  
Berend Wierenga
Keyword(s):  
Least Squares ◽  
Simulation Study ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Estimation Method ◽  
Learning Model ◽  
New Method ◽  
Data Sets ◽  
Goodness Of Fit Test ◽  
Chi Square

The author presents a new method for estimating the parameters of the linear learning model. The procedure, essentially a least squares method, is easy to carry out and avoids certain difficulties of earlier estimation procedures. Applications to three different data sets are reported, as well as results from a goodness-of-fit test. A simulation study was carried out to validate the method. The outcomes are compared with those obtained from the minimum chi square estimation method. The results of the new method appear to be satisfactory.

scholarly journals A Recursive Least Squares Method with Double-Parameter for Online Estimation of Electric Meter Errors

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 805 ◽  
Author(s):  
Xiangyu Kong ◽  
Yuying Ma ◽  
Xin Zhao ◽  
Ye Li ◽  
Yongxing Teng
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Measured Data ◽  
Recursive Least Squares ◽  
Maintenance Cost ◽  
Parameter Method ◽  
Online Estimation ◽  
Light Load ◽  
Verification Methods ◽  
Line Loss

In view of the existing verification methods of electric meters, there are problems such as high maintenance cost, poor accuracy, and difficulty in full coverage, etc. Starting from the perspective of analyzing the large-scale measured data collected by user-side electric meters, an online estimation method for the operating error of electric meters was proposed, which uses the recursive least squares (RLS) and introduces a double-parameter method with dynamic forgetting factors λa and λb to track the meter parameters changes in real time. Firstly, the obtained measured data are preprocessed, and the abnormal data such as null data and light load data are eliminated by an appropriate clustering method, so as to screen out the measured data of the similar operational states of each user. Then equations relating the head electric meter in the substation and each users’ electric meter and line loss based on the law of conservation of electric energy are established. Afterwards, the recursive least squares algorithm with double-parameter is used to estimate the parameters of line loss and the electric meter error. Finally, the effects of double dynamic forgetting factors, double constant forgetting factors and single forgetting factor on the accuracy of estimated error of electric meter are discussed. Through the program-controlled load simulation system, the proposed method is verified with higher accuracy and practicality.

Analysis of the fatigue life estimators of the materials using small samples

2018 ◽  
Vol 53 (8) ◽  
pp. 699-710 ◽  
Author(s):  
Joelton Fonseca Barbosa ◽  
Raimundo Carlos Silverio Freire Júnior ◽  
Jose AFO Correia ◽  
Abilio MP De Jesus ◽  
RAB Calçada
Keyword(s):  
Fatigue Life ◽  
Least Squares ◽  
Goodness Of Fit ◽  
Failure Time ◽  
Fatigue Behavior ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Least Squares Estimation ◽  
Small Samples ◽  
Weighted Least Squares Estimation

Knowledge of the stochastic nature of fatigue life of composite materials can be modeled by the failure time with the Weibull distribution. This task becomes complex when the samples are small and scattered. In this way, it is necessary to know and to improve robust models of estimation of the parameters of the distribution of Weibull. The aim of this work is to compare the performance of least squares, least squares weighted, maximum likelihood estimator and momentum method and to suggest a method that obtains better performance in life behavior to fatigue with small samples. Monte Carlo simulations were performed to estimate the distribution parameters with different sample sizes and an application with real fatigue data that compares performance using goodness-of-fit. The results of the simulations showed that the weighted least squares estimation was able to generate more reliable estimators for fatigue behavior during its useful life. In this way, it is possible to conclude that small samples make the real representation of life difficult to the material fatigue, but using the weighted least squares estimation method, it is possible to obtain more estimates.

scholarly journals Estimating Regression Function of Multi-response Semiparametric Regression Model Using Smoothing Spline

2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Budi Lestari ◽  
Nur Chamidah
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Goodness Of Fit ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Semiparametric Regression ◽  
Regression Function ◽  
Smoothing Spline ◽  
Semiparametric Regression Model ◽  
Research Results

The article describes a new estimation method of regression functions in a multi-response semiparametric regression model based on smoothing spline. The multi-response semiparametric regression model is a combined model between a parametric regression model and a nonparametric regression model, where there is a correlation between responses. The proposed estimation method enhances the flexibility of the multi-response semiparametric regression model by combining a goodness of fit function and a penalty function to calculate an estimation which not only considers the goodness of fitting of the model, but also the smoothness of the estimation model curve. The optimal trade-off between goodness and smoothness can be achieved by selecting the optimal smoothing parameters. The article discusses a theoretically proposed method for estimating this multi-response semiparametric regression model regression function of parametric and nonparametric components. We use the weighted least squares method to estimate the parametric component parameters, we determine the goodness of fit and penalty functions using the reproducing kernel Hilbert space method, and then take the result of penalized weighted least squares optimization to obtain an estimate of the nonparametric component. The new research results are a weighted least squares estimator of parameters of parametric components, and a weighted partial smoothing spline estimator of the nonparametric component. The result shows that the estimated multi-response semiparametric regression model is linear to the observation, and is a combination of the estimations of the parametric and nonparametric components. The research results of the estimation of this model can be applied to medical fields for predictive purposes.

scholarly journals Modeling and identification of heat exchanger process using least squares support vector machines

2017 ◽  
Vol 21 (6 Part B) ◽  
pp. 2859-2869 ◽  
Author(s):  
Mujahed Al-Dhaifallah ◽  
Kottakkaran Nisar ◽  
Praveen Agarwal ◽  
Alaa Elsayyad
Keyword(s):  
Heat Exchanger ◽  
Support Vector Machines ◽  
Least Squares ◽  
A Priori ◽  
Hammerstein Model ◽  
Support Vector ◽  
Tubular Heat Exchanger ◽  
Accuracy And Precision ◽  
Vector Machines ◽  
Narx Model

In this paper, Hammerstein model and non-linear autoregressive with eXogeneous inputs (NARX) model are used to represent tubular heat exchanger. Both models have been identified using least squares support vector machines based algorithms. Both algorithms were able to model the heat exchanger system with-out requiring any a priori assumptions regarding its structure. The results indicate that the blackbox NARX model outperforms the NARX Hammerstein model in terms of accuracy and precision.

scholarly journals A Battery SOC Estimation Method Based on AFFRLS-EKF

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5698
Author(s):  
Ming Li ◽  
Yingjie Zhang ◽  
Zuolei Hu ◽  
Ying Zhang ◽  
Jing Zhang
Keyword(s):  
Least Squares ◽  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Forgetting Factor ◽  
Battery Charge ◽  
Estimation Strategy ◽  
Soc Estimation

The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares–extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

Parameters Recursive Identification of Minimum Variance Control

2013 ◽  
Vol 347-350 ◽  
pp. 15-18 ◽  
Author(s):  
Zhi Hua Dai ◽  
Yu An Pan ◽  
Jie Yao
Keyword(s):  
System Identification ◽  
Least Squares ◽  
Input Signal ◽  
Minimum Variance ◽  
Recursive Least Squares ◽  
Recursive Identification ◽  
Minimum Variance Control ◽  
Optimal Input ◽  
Identification Method ◽  
On Line

we discuss the problem of parameters recursive identification and designing of optimal input signal for minimum variance control from the point of system identification. we propose multi-innovation recursive least-squares identification method and separable iterative recursive least-squares identification method to identify and estimate it on line. Finally, the efficiency and possibility of the proposed strategy can be confirmed by the simulation example results.

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