Abstract
Three-parameter Weibull distribution is widely employed as a model in reliability and lifetime studies due to its good fit to data. It is important to estimate the unknown parameters exactly for modeling. There are many methods to estimate the parameters of three-parameter Weibull distribution and the kernel density estimation method is one of them. The smoothing parameter has a significant influence on the estimation accuracy. In this paper, the neural network and genetic algorithm were used to get the best smoothing parameter and the result was compared with other methods. The Monte Carlo simulations were carried out to show the feasibility of our approach for estimation of three-parameter Weibull distribution.
| Original language | English |
|---|---|
| Pages (from-to) | 803-814 |
| Number of pages | 12 |
| Journal | Applied Mathematics and Computation |
| Volume | 247 |
| DOIs | |
| State | Published - 15 Nov 2014 |
Keywords
- Genetic algorithm
- Grey model
- Maximum likelihood method
- Neural network model
- Optimization algorithm
- Weibull distribution