Using neural network to estimate weibull parameters
As is well known, estimating parameters of the tree-parameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. Weibull distribution involves in reliability studies frequently and has many applications in engineering. However estima...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Islamic Azad University, Qazvin Branch
2010-02-01
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| Series: | Journal of Optimization in Industrial Engineering |
| Subjects: | |
| Online Access: | http://www.qjie.ir/article_26_54d474e57f129c0a6feba41b63cf7a62.pdf |
| _version_ | 1818760314260291584 |
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| author | Babak Abbasi behrouz Afshar nadjafi |
| author_facet | Babak Abbasi behrouz Afshar nadjafi |
| author_sort | Babak Abbasi |
| collection | DOAJ |
| description | As is well known, estimating parameters of the tree-parameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. Weibull distribution involves in reliability studies frequently and has many applications in engineering. However estimating the parameters of Weibull distribution is crucial in classical ways. This distribution has three parameters, but for simplicity, a parameter is ridded off and as a result, the estimation of the others will be easily done. When the three-parameter distribution is of interest, the classical estimation procedures such as maximum likelihood estimation (MLE) will be quite boring. In this paper to take advantage of application of artificial neural networks (ANN) to statistics, we propose using a simple neural network to estimate three parameters of Weibull distribution simultaneously. Trained neural network similar to moment method estimates Weibull parameters based on mean, standard deviation, median, skewness and kurtosis of the sample accurately. To assess the power of the proposed method we carry out simulation study and compare the results of the proposed method with real values of the parameters. |
| first_indexed | 2024-12-18T06:56:39Z |
| format | Article |
| id | doaj.art-fa9ae92c115d4dbc93c4a35058c06933 |
| institution | Directory Open Access Journal |
| issn | 2251-9904 2423-3935 |
| language | English |
| last_indexed | 2024-12-18T06:56:39Z |
| publishDate | 2010-02-01 |
| publisher | Islamic Azad University, Qazvin Branch |
| record_format | Article |
| series | Journal of Optimization in Industrial Engineering |
| spelling | doaj.art-fa9ae92c115d4dbc93c4a35058c069332022-12-21T21:17:11ZengIslamic Azad University, Qazvin BranchJournal of Optimization in Industrial Engineering2251-99042423-39352010-02-01Volume 1Issue 1485426Using neural network to estimate weibull parametersBabak Abbasi0behrouz Afshar nadjafi1Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran,Department of Industrial Engineering, Islamic Azad University of Qazvin, Qazvin, Iran,As is well known, estimating parameters of the tree-parameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. Weibull distribution involves in reliability studies frequently and has many applications in engineering. However estimating the parameters of Weibull distribution is crucial in classical ways. This distribution has three parameters, but for simplicity, a parameter is ridded off and as a result, the estimation of the others will be easily done. When the three-parameter distribution is of interest, the classical estimation procedures such as maximum likelihood estimation (MLE) will be quite boring. In this paper to take advantage of application of artificial neural networks (ANN) to statistics, we propose using a simple neural network to estimate three parameters of Weibull distribution simultaneously. Trained neural network similar to moment method estimates Weibull parameters based on mean, standard deviation, median, skewness and kurtosis of the sample accurately. To assess the power of the proposed method we carry out simulation study and compare the results of the proposed method with real values of the parameters.http://www.qjie.ir/article_26_54d474e57f129c0a6feba41b63cf7a62.pdfArtificial neural networksparameters estimatetree- parameter weibull |
| spellingShingle | Babak Abbasi behrouz Afshar nadjafi Using neural network to estimate weibull parameters Journal of Optimization in Industrial Engineering Artificial neural networks parameters estimate tree- parameter weibull |
| title | Using neural network to estimate weibull parameters |
| title_full | Using neural network to estimate weibull parameters |
| title_fullStr | Using neural network to estimate weibull parameters |
| title_full_unstemmed | Using neural network to estimate weibull parameters |
| title_short | Using neural network to estimate weibull parameters |
| title_sort | using neural network to estimate weibull parameters |
| topic | Artificial neural networks parameters estimate tree- parameter weibull |
| url | http://www.qjie.ir/article_26_54d474e57f129c0a6feba41b63cf7a62.pdf |
| work_keys_str_mv | AT babakabbasi usingneuralnetworktoestimateweibullparameters AT behrouzafsharnadjafi usingneuralnetworktoestimateweibullparameters |