改进回溯搜索优化回声状态网络时间序列预测

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.

Time Series Forecasting Based on Echo State Network Optimized by Improved Backtracking Search Optimization Algorithm

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.