Building Heat Load Estimation Method Including Parameter Estimation from Actual Data

The Bass model is the most popular model for forecasting the diffusion process of a new product. However, the controlling parameters in it are unknown in practice and need to be determined in advance. Currently, the estimation of the controlling parameters has been approached by various techniques. In this case, a novel optimization-based parameter estimation (OPE) method for the Bass model is proposed in the theoretical framework of system dynamics ( SD). To do this, the SD model of the Bass differential equation is first established and then the corresponding optimization mathematical model is formulated by introducing the controlling parameters as design variable and the discrepancy of the adopter function to the reference value as objective function. Using the VENSIM software, the present SD optimization model is solved, and its effectiveness and accuracy are demonstrated by two examples: one involves the exact solution and another is related to the actual user diffusion problem from Chinese Mobile. The results show that the present OPE method can produce higher predicting accuracy of the controlling parameters than the nonlinear weighted least squares method and the genetic algorithms. Moreover, the reliability interval of the estimated parameters and the goodness of fitting of the optimal results are given as well to further demonstrate the accuracy of the present OPE method.

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MLE-Based Parameter Estimation for Four-Parameter Exponential Gamma Distribution and Asymptotic Variance of Its Quantiles

Water ◽

10.3390/w13152092 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2092

Author(s):

Songbai Song ◽

Yan Kang ◽

Xiaoyan Song ◽

Vijay P. Singh

Keyword(s):

Parameter Estimation ◽

Confidence Intervals ◽

Asymptotic Variance ◽

Information Matrix ◽

Estimation Method ◽

Flood Frequency Analysis ◽

Annual Precipitation ◽

Precipitation Data ◽

Marquardt Algorithm ◽

Design Values

The choice of a probability distribution function and confidence interval of estimated design values have long been of interest in flood frequency analysis. Although the four-parameter exponential gamma (FPEG) distribution has been developed for application in hydrology, its maximum likelihood estimation (MLE)-based parameter estimation method and asymptotic variance of its quantiles have not been well documented. In this study, the MLE method was used to estimate the parameters and confidence intervals of quantiles of the FPEG distribution. This method entails parameter estimation and asymptotic variances of quantile estimators. The parameter estimation consisted of a set of four equations which, after algebraic simplification, were solved using a three dimensional Levenberg-Marquardt algorithm. Based on sample information matrix and Fisher’s expected information matrix, derivatives of the design quantile with respect to the parameters were derived. The method of estimation was applied to annual precipitation data from the Weihe watershed, China and confidence intervals for quantiles were determined. Results showed that the FPEG was a good candidate to model annual precipitation data and can provide guidance for estimating design values

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Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

Journal of Electrical and Computer Engineering ◽

10.1155/2015/168786 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Jian-wei Yang ◽

Man-feng Dou ◽

Zhi-yong Dai

Keyword(s):

Parameter Estimation ◽

Fault Diagnosis ◽

Permanent Magnet ◽

Fault Tolerant ◽

Short Circuit ◽

Estimation Method ◽

Trust Region ◽

Fault Tolerant Control ◽

Trust Region Algorithm ◽

Parameter Estimation Method

Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

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Estimation of Failure Probability and its Applications in Reliability Data Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.118-120.601 ◽

2010 ◽

Vol 118-120 ◽

pp. 601-605

Author(s):

Han Ming

Keyword(s):

Parameter Estimation ◽

Data Analysis ◽

Bayesian Estimation ◽

Failure Probability ◽

Evaluation Method ◽

Estimation Method ◽

New Method ◽

Reliability Parameter ◽

Hierarchical Bayesian Estimation ◽

Definition Of

Evaluation method of reliability parameter estimation needs to be improved effectively with the advance of science and technology. This paper develops a new method of parameter estimation, which is named E-Bayesian estimation method. In the case one hyper-parameter, the definition of E-Bayesian estimation of the failure probability is provided, moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation, and the property of E-Bayesian estimation of the failure probability are also provided. Finally, calculation on practical problems shows that the provided method is feasible and easy to perform.

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