| 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） |
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| Application of BP neural network based on particle swarm optimization in seawater quality assessment |
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LI Hai-tao, WANG Bo-rui
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College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
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| 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 |
