Application of Water Quality Prediction Based on MEC-BP Neural Network in Aquaculture

doi:10.3969/j.issn.1006-2475.2015.06.007

Abstract

Abstract: Dissolved oxygen as the most important and the most easily control water quality parameter of aquaculture relates to the success or failure of aquaculture. If we can accurately grasp the change rule of dissolved oxygen, the risk of breeding will greatly reduce and the breeding success rate will increase. In this paper, considering the related water quality parameters, we established the BP neural network model of water quality prediction. And on this basis, the BP neural network model based on mind evolutionary algorithm (MEC) of water quality prediction was established. Through the simulation of historical data of water quality of Maowei Sea, Guangxi, the results show that the precision and accuracy of predicted value of MECBP neural network is higher than that of the BP neural network. Therefore, it is feasible to apply this algorithm to predict water quality of aquaculture.

Key words: dissolved oxygen, water quality prediction, BP neural network, Mind Evolutionary Computation

CLC Number:

X832

YANG Zheng-guang, FAN Liang-zhong. Application of Water Quality Prediction Based on MEC-BP Neural Network in Aquaculture[J]. Computer and Modernization, 2015, 0(6): 32-36.

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