Employing Intelligent Methods to Estimate the Parameters of the Proposed Generalized Goel Process.
Abstract
This paper will present the Goel distribution as the occurrence rate of the non-homogeneous Poisson process (NHPP) to improve its occurrence rate, it is proposed to be called the Generalized Goel Process (GGP). As for the estimation of parameters of this process, a number of methods were discussed, the maximum likelihood estimator (MLE) was suggested and after that a modification to this method was necessary due to the fact that it was impossible to find estimators using it. An intelligent algorithm of the likelihood function was added with the parameter and was known as the Modified Maximum Likelihood Estimator (MMLE). MMLE was then compared with another intelligent method the Particle Swarm Optimization (PSO) in estimating occurrence rate of the proposed Goel process to determine the best estimator of the process. Besides, the paper contains the simulation of the mentioned process and an example of its practical usage. The simulation and application results showed that the MMLE approach gave higher accuracy estimates than the PSO algorithm for the majority of the studied sample sizes, especially for the larger sizes.
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