回声状态网络
计算机科学
时间序列
系列(地层学)
可预测性
理论(学习稳定性)
仿生学
光谱半径
网络拓扑
算法
重现图
跳跃
控制理论(社会学)
生物系统
人工智能
循环神经网络
数学
人工神经网络
机器学习
非线性系统
统计
物理
古生物学
特征向量
控制(管理)
量子力学
生物
操作系统
作者
Yuanpeng Gong,Shuxian Lun,Ming Li,Xiaodong Lu
标识
DOI:10.1016/j.asoc.2024.111257
摘要
From the perspective of bionics of biological structure, this paper proposes a new reservoir topology structure with an α-helix form of the secondary protein, named S-ESN. This network model has some advantages compared with the standard leaky-echo state network (Leaky-ESN) model. Because the neurons in the traditional reservoir are randomly and sparsely connected, the stability of the echo state network (ESN) will be reduced, and the prediction accuracy will also be decreased. The S-ESN model proposed greatly improves the internal stability of the reservoir, the dynamic activity of neurons and the prediction accuracy of the ESN. At the same time, the improved moth-flame optimization algorithm (MFO) with the probability of jump disturbance is used to optimize the three parameters: the leakage rate (a), the spectral radius (ρ), and the input scaling factor (sin), which can further improve the stability and predictability of the S-ESN. In order to verify the performance of S-ESN, three virtual time series Sin time series with low frequency, Sin time series with high frequency, Mackey-Glass time series (MG) and one practical Sunspot are selected as experimental data. The experimental results show that the S-ESN model has better prediction accuracy.
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