Title

Estimating Parameters Of The Three-Parameter Weibull Distribution Using A Neural Network

Keywords

ANN; Artificial Neural Network; Maximum Likelihood Estimation; MLE; Moment method; Parameter estimation; Three-parameter Weibull distribution

Abstract

Weibull distributions play an important role in reliability studies and have many applications in engineering. It normally appears in the statistical scripts as having two parameters, making it easy to estimate its parameters. However, once you go beyond the two parameter distribution, things become complicated. For example, estimating the parameters of a three-parameter Weibull distribution has historically been a complicated and sometimes contentious line of research since classical estimation procedures such as Maximum Likelihood Estimation (MLE) have become almost too complicated to implement. In this paper, we will discuss an approach that takes advantage of Artificial Neural Networks (ANN), which allow us to propose a simple neural network that simultaneously estimates the three parameters. The ANN neural network exploits the concept of the moment method to estimate Weibull parameters using mean, standard deviation, median, skewness and kurtosis. To demonstrate the power of the proposed ANN-based method we conduct an extensive simulation study and compare the results of the proposed method with an MLE and two moment-based methods. © 2008, Inderscience Publishers.

Publication Date

1-1-2008

Publication Title

European Journal of Industrial Engineering

Volume

2

Issue

4

Number of Pages

428-445

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1504/EJIE.2008.018438

Socpus ID

44449166051 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/44449166051

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