Application of Genetic Algorithm-Based Artificial Neural Network in Prediction of Aircraft Engine Wear

Xu-feng, Jiang; Guo, Chao; Zhang, Yuwei; Jian-bo, Wang

doi:10.1109/icdma.2010.65

Cited by 1 publication

(3 citation statements)

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“…In [28] is one of the examples that does so. Apart from K-means, genetic algorithm is one other frequently used algorithm which was used for parameter optimization [29][30][31][32][33]. Genetic algorithm optimizes parameters that will be input to the prediction algorithms.…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

“…It is shown that with the help of genetic algorithm, it obtains a better accuracy. In [31] and [32] also combines genetic algorithm with neural network for their prediction model. The former predicts train arrival time and the latter predicts aircraft engine wear.…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

“…As the work mentioned before, the genetic algorithm plays an important role in these cases where it optimizes the parameters for the neural network algorithm [29]. In a previous work done, they are able to solve the problem of low speed in local optimization and convergence of backpropagation neural network [23] and another one was able to achieve high accuracy for their prediction with the deployment of genetic algorithm [31].…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

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Systematic review on event prediction models

Jamaludin

Ramasamy

2020

IJEECS

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Predicting an event can comes as a huge advantage for a lot of people, especially if it involves profits that a company can make. In this paper, matters that were discussed were the algorithms used for the event prediction systems, the type of algorithms and the comparison between different types of algorithm. Two main categories that were compared was the single algorithm for the prediction model and hybrid algorithms which is a combination of two or more algorithms in the model. Hybrid algorithm shows signs of better efficiency when data used were complex and large but a smaller dataset is more suitable with a single algorithm as the base as it is easier to apply with satisfactory results.

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