摘要
回声状态网络(Echo State Network, ESN)网络结构简单且耦合"时间参数",在时间序列预测研究中具有重要的理论和应用价值.本文提出使用自适应回溯搜索算法(Adaptive Backtracking Search optimization Algorithm,ABSA)优化ESN输出连接权值矩阵,克服标准线性回归方法造成的网络过拟合问题. ABSA使用自适应变异因子策略替换标准BSA中随机给定变异因子的策略,实现BSA在收敛精度和收敛速率之间的平衡.实验表明,采用ABSA优化的ESN能够比未优化的ESN和采用其他进化算法优化的ESN获得更好的预测精度.
Echo State Network(ESN) owns simple network structure and is coupled with a time parameter and thus it shows important theoretical and application values in time series forecasting. In this study, we propose to optimize the output weight matrix by Adaptive Backtracking Search optimization Algorithm(ABSA) to overcome overfitting problem caused by linear regression algorithm. ABSA adopts adaptive mutation factor strategy to replace the strategy of randomly given mutation factor in standard BSA to achieve the balance between convergence accuracy and convergence rate.Experimental results show that the ESN optimized by ABSA outperforms the basic ESN without optimization and the ESNs optimized by other EAs.
作者
胡率
肖治华
饶强
廖荣涛
HU Shuai;XIAO Zhi-Hua;RAO Qiang;LIAO Rong-Tao(Information&Communication Branch,State Grid Hubei Electric Power Co.Ltd.,Wuhan 430077,China)
出处
《计算机系统应用》
2020年第1期236-243,共8页
Computer Systems & Applications
基金
国网湖北省电力有限公司科技项目(52153317000B)~~
关键词
时间序列预测
回声状态网络
回溯搜索算法
预测模型
进化算法
time series forecasting
Echo State Network(ESN)
Adaptive Backtracking Search optimization Algorithm(ABSA)
forecasting model
evolutionary algorithm
