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引用本文:李海涛,王博睿.基于粒子群算法优化的BP神经网络在海水水质评价中的应用[J].海洋科学,2020,44(6):31-36.
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基于粒子群算法优化的BP神经网络在海水水质评价中的应用
李海涛, 王博睿
青岛科技大学 信息科学与技术学院, 山东 青岛 266061
摘要:
针对目前存在的海水水质受多因素影响、评价难的现状,提出了一种基于粒子群算法(PSO)优化误差反向传播(BP)神经网络的海水水质评价模型。该模型通过PSO得到BP神经网络最优的权值和阈值,结合青岛东部海域10个监测站点的数据得到水质评价结果。实验证明,该模型和单因子评价、传统的BP神经网络评价相比较,具有训练时间短、预测精度高的特点,在海水水质评价中具有良好的应用价值。
关键词:  粒子群算法  BP神经网络  海水水质评价
DOI:10.11759/hykx20191116002
分类号:TP391.9
基金项目:农业部水产养殖数字农业建设试点项目(2017-A2131-130209-K0104-004)
Application of BP neural network based on particle swarm optimization in seawater quality assessment
LI Hai-tao, WANG Bo-rui
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Abstract:
To address the current situation in which seawater quality is affected by many factors and is difficult to evaluate, a seawater quality assessment model based on particle swarm optimization (PSO) optimized-error backpropagation (BP) neural network is proposed. The model uses the optimal weight and threshold of a BP neural network through PSO to obtain water quality evaluation results based on data from 10 monitoring stations in the eastern sea area of Qingdao. Experiments show that the model has a shorter training time and higher prediction accuracy compared with single-factor evaluation and traditional BP neural network evaluation. Overall, the proposed model has good application value in seawater quality assessment.
Key words:  particle swarm optimization  BP neural network  seawater quality assessment
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