摘要
为准确预测城市自来水供水量,提出采用教与学优化算法(TLBO)优化的极限学习机预测方法。针对TLBO算法收敛精度低、易陷入局部最优的不足,提出一种改进的TLBO算法(ITLBO)。在ITLBO中,增加一个最差学生补习阶段,通过老师对该学生单独辅导或者采用一个反向学习策略快速提升学生成绩;在此基础上,采用一种干扰算子对老师进行扰动,增强种群跳出局部最优的动能;最后,将ITLBO算法用于优化调整极限学习机(ELM)模型的输入权值和隐层阈值参数,并构建ITLBO-ELM自来水供水量预测模型。将ITLBO-ELM模型用于上海市自来水供水量的预测实验,仿真结果表明该模型能够准确预测自来水供水量。
In order to predict the total amount of city tap water supply accurately,a predicting method of the extreme learning machine(ELM)optimized by teaching⁃learning⁃based optimization was proposed.An improved TLBO algorithm(ITLBO)was proposed to solve the problem of low convergence accuracy and easy to fall into local optimization.In ITLBO,an extra tutoring stage was added for the worst student,and the teacher could help the student individually or adopt the opposition⁃based learning strategy to quickly improve the student's performance.On this basis,a disturbance operator was used to perturb the teacher position,which increased the kinetic energy of the popula⁃tion to jump out of the local optimum.Finally,the improved ITLBO algorithm was used to optimize and adjust the input weight and hidden threshold parameters of ELM model,and the ITLBO⁃ELM water supply prediction model was built.ITLBO⁃ELM model was used to predict the tap water supply in Shanghai.The simulation results show that the model can accurately predict the tap water supply total amount.
作者
左智科
李一龙
ZUO Zhike;LI Yilong(Chongqing Key Laboratory of Spatial Data Mining and Big Data Integration for Ecology and Environment,Chongqing Finance and Economics College,Chongqing 401320,China;Jiangxi College of Engineering,Xinyu 338000,China)
出处
《人民黄河》
CAS
北大核心
2021年第2期84-87,共4页
Yellow River
基金
重庆市教委科学技术研究项目(KJZD-K201902101)。
关键词
预测
极限学习机
教与学优化算法
反向学习
优化
prediction
extreme learning machine
teaching⁃learning⁃based optimization
opposition⁃based learning
optimization
