CAJ | 학술논문

为了提高回声状态网对时间序列的预测精度,将改进的小世界网络和泄露积分型回声状态网结合,提出了一种新型时间序列预测方法.泄露积分型回声状态网储备池神经元采用随机网络进行连接,首先利用改进的小世界网络替代随机网络,提高了储备池的适应性,从而改善回声状态网的泛化能力和稳定性.然后将利用改进的回声状态网预测典型的非线性时间序列.最后利用Matlab仿真软件进行验证,仿真结果表明,该方法较传统回声状态网预测模型具有更高的效率和预测精度.
위료제고회성상태망대시간서렬적예측정도,장개진적소세계망락화설로적분형회성상태망결합,제출료일충신형시간서렬예측방법.설로적분형회성상태망저비지신경원채용수궤망락진행련접,수선이용개진적소세계망락체대수궤망락,제고료저비지적괄응성,종이개선회성상태망적범화능력화은정성.연후장이용개진적회성상태망예측전형적비선성시간서렬.최후이용Matlab방진연건진행험증,방진결과표명,해방법교전통회성상태망예측모형구유경고적효솔화예측정도.
In order to improve the prediction accuracy of echo state network for time series, this paper pro-poses an optimization method by combining modified small world network with the leaky echo state network.The neurons of reservoir of leaky echo state network adopt randomly connected network.Firstly,using the modified small world network instead of random network, A small world network is used to improve connection mode of reservoir processing unit and boost the adaptability of reservoir,thus generalization ability and stability of echo state network are improved.Next,the improved echo state network model is used to predict the typical nonlinear time series.Finally,Matlab software is used to verify in this paper.The simulation results show that the method proposed in this paper has faster convergence speed and higher precision than the traditional echo state network .