2019
DOI: 10.1109/jas.2019.1911753
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Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network

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Cited by 58 publications
(30 citation statements)
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“…e value for random seed is explained specifically by the Gaussian random variables samples. (iii) irdly, the study of the nonparametric density estimation (18) with the normal kernel algorithm and multivariate density function have been generally completed to calculate the transitional density (17). (iv) en, it is necessary to repeat the above step for each time point t 0 , .…”
Section: Simulated Maximum Likelihood Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…e value for random seed is explained specifically by the Gaussian random variables samples. (iii) irdly, the study of the nonparametric density estimation (18) with the normal kernel algorithm and multivariate density function have been generally completed to calculate the transitional density (17). (iv) en, it is necessary to repeat the above step for each time point t 0 , .…”
Section: Simulated Maximum Likelihood Methodsmentioning
confidence: 99%
“…PSO characterized as an artificial intelligence (AI) method is used to make a process of approximation of the minimization problems, which is a sort of nondifferentiable optimization problem in order to arrive at a solution. A number of comparison studies have been conducted to investigate the efficiency of PSO and GA [17][18][19][20][21][22][23]. Also, particle swarm optimization provides an important way in fine-tuning the parameters of finance models and deserved popularity in this field [24][25][26][27][28][29].…”
Section: Introductionmentioning
confidence: 99%
“…And some research studies [39,40] also applied more advanced particle swarm optimization variants in the neural network, burden distribution matrix, and other aspects.…”
Section: Solution Methodologymentioning
confidence: 99%
“…The CSO is a kind of group intelligence that improves PSO to face large-scale classification problems. It is a mechanism for comparing the evaluation results of different particles selected from the population; only the failed particles are learned to update [45]. Therefore, in addition to the number of updated particles being able to be reduced to 2/N, the excellent solutions in the search do not need to be saved, and it can be used for efficient search on large-scale classification problems.…”
Section: Competitive Swarm Optimizermentioning
confidence: 99%