Grey neural network prediction model based on fruit fly optimisation algorithm and its application
by Yang Jing; Zeng Hui; Huang Jiangping
International Journal of Information and Communication Technology (IJICT), Vol. 12, No. 1/2, 2018

Abstract: The vinyl acetate (VAC) polymerisation rate is considered as an important quality index in the production of polyvinyl alcohol. However, the quality of polyvinyl alcohol can not be controlled effectively because it can not be measured online. Therefore, how to design reliable estimation of VAC polymerisation rate is crucial. The fruit fly optimisation algorithm (FOA), as a novel meta-heuristic and evolutional algorithm, has several merits, such as higher prediction accuracy, having few parameters to be adjusted and able to achieve global optimum. This paper proposes a grey neural network prediction model to improve the prediction performance by taking advantage of FOA to optimise the 'whitening' parameters of this grey neural network. Based on the data from a real plant, the proposed grey neural network prediction model combined with FOA (FOA_GNN) is evaluated and proved to be valid. Its simulation experimental results also confirm the advantage of FOA_GNN algorithm to the traditional grey neural network model (GNN), the adaptive compete genetic neural network prediction model (ACGA) and radial basic function (RBF) neural network model.

Online publication date: Thu, 04-Jan-2018

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