摘要
针对BP神经网络预测精度不佳、预测结果不稳定的问题,提出改进黏菌算法(ISMA)优化BP神经网络的预测模型,引入Tent混沌映射克服初始种群分布不均的缺点,针对黏菌算法位置更新的随机性和后期容易陷入局部最优等问题引入领导者策略和莱维飞行策略,利用自适应反向学习策略扩大搜索空间并用23组基准函数加以测试。随后利用ISMA算法优化BP网络模型的初始权值和阈值,构建ISMA-BP空气质量指数预测模型,最后将收集到的779组空气质量指数数据代入预测模型中进行测试分析,实验结果表明,与BP神经网络模型、GWO-BP、SMA-BP模型相比,ISMA-BP模型对AQI的预测具有更高的精度,其预测的均方误差为3.8402,平均绝对误差分别为1.5078。
Aiming at the problems of poor prediction accuracy and unstable prediction results of BP neural network,an improved slime mold algorithm(ISMA)is proposed to optimize the prediction model of BP neural network,and Tent chaotic mapping is introduced to overcome the shortcomings of uneven initial population distribution.The leader strategy and Levy flight strategy are introduced to solve the randomness of the position update and the problem of falling into local optimality.The adaptive reverse learning strategy is used to expand the search space and 23 groups of benchmark functions are tested.Then the ISMA algorithm was used to optimize the initial weights and thresholds of the BP network model,and the ISMA-BP Air quality index prediction model was constructed.At last,779 sets of AQI data were collected and put into the prediction model for testing and analysis.The experimental results showed that,Compared with BP neural network model,GWO-BP model and SMA-BP model,ISMA-BP model has higher accuracy in predicting AQI.The mean square error of ISMA-BP model is 3.8402,and the mean absolute error is 1.5078 respectively.
作者
文昌俊
陈洋洋
何永豪
陈凡
Wen Changjun;Chen Yangyang;He Yonghao;Chen Fan(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China;Hubei Province Key Laboratory of Modern Manufacturing Quality Engineering,Wuhan 430068,China)
出处
《电子测量技术》
北大核心
2023年第22期78-86,共9页
Electronic Measurement Technology
基金
国家自然科学基金(51875180)项目资助
作者简介
文昌俊,博士,教授,研究生导师,主要研究方向为质量管理与可靠性技术。E-mail:794411947@qq.com;通信作者:陈洋洋,硕士研究生,主要研究方向为质量管理与可靠性分析。E-mail:1293401250@qq.com;何永豪,硕士研究生,主要研究方向为质量管理与可靠性分析。陈凡,硕士研究生,主要研究方向为质量管理与可靠性分析。
