摘要
数据缺失降低了数据的可利用性,因此如何预测缺失数据变得尤为重要。针对缺失数据问题,提出一种改进麻雀搜索算法优化深度极限学习机的预测算法。首先,将Singer混沌映射、柯西-高斯变异策略和余弦权重因子与麻雀搜索算法结合;其次利用改进后的麻雀搜索算法优化深度极限学习机中的各极限学习机中自动编码器的输入权重与偏置,进行缺失数据预测。实验表明,在小数据量,低缺失率下时,改进麻雀搜索算法优化深度极限学习机相较于麻雀搜索算法优化深度极限学习机、粒子群优化深度极限学习机、深度极限学习机,其稳定性强,预测精度最高;在均方根误差、平均绝对误差等评价指标上改进麻雀搜索算法优化深度极限学习机优于对比算法。
Missing data reduces data availability. Prediction of missing data becomes very important. A prediction algorithm named ISSA-DELM(Improved Sparrow Search Algorithm optimized Deep Extreme Learning) was proposed to solve the problem of missing data. First of all, singer chaotic map, Cauchy-Gaussian mutation strategy and cosine weight factor combined with sparrow search algorithm. Secondly, the input weights and biases of the autoencoders in each extreme learning machine in the deep extreme learning machine are optimized by ISSA. Then ISSA-DELM is applied to predict missing data. The experimental results show that, ISSA-DELM has strong stability and the highest prediction accuracy compared with SSA-DELM、Particle Swarm Optimization DELM(PSO-DELM)、DELM in the case of small data volume and low miss rate. The evaluation indexes, such as RMSE, MAE and the coefficient of determination are better than the compared algorithms.
作者
张文帅
王占刚
Zhang Wenshuai;Wang Zhangang(School of Information and Communication Engineering,Bejing Information Seience&,Technology University,Bejing 100020,China)
出处
《电子测量技术》
北大核心
2022年第15期63-67,共5页
Electronic Measurement Technology
基金
国家重点研发计划课题资助(2018YFC1800203)
北京市科技创新服务能力建设-基本科研业务费(市级)(科研类)(PXM2019_014224_000026)资助。
关键词
缺失数据预测
深度极限学习机
麻雀搜索算法
混沌映射
变异策略
missing data prediction
deep extreme learning machine
sparrow search algorithm
chaotic mapping
mutation strategy
