用于混沌时间序列预测的多簇回响状态网络

研究了混沌时间序列预测问题.提出了一种由五元生长因子组调控的类皮层神经网络模型，即多簇回响状态网络模型（MCESN）.研究表明该生长因子组能够有效决定模型的拓扑性质；同时具备小世界和无标度等复杂网络特征的MCESN能够获得较优的预测结果.通过Monte Carlo仿真实验表明，该模型不仅训练算法简单，而且与常规回响状态网络比较，预测结果的精度更高、标准差更小. 关键词： 混沌时间序列预测 回响状态网络 复杂网络 Ω复杂性')" href="#">Ω复杂性

Multiple clusters echo state network for chaotic time series prediction

The chaotic time series prediction problem is considered. A novel type of cortex-like neural network model, i.e. multi-clusters echo state network model （MCESN）, regulated by a group of five growth-factors, is proposed. It is shown that characters of MCESN’ topology can be effectively determined by the growth-factors group; and that it is the MCESN possessing both small-world and scale-free properties of complex network that corresponds to the better prediction performance. In addition, Monte Carlo simulation experiments show that MCESN not only can be trained by easy algorithm, but also can achieve higher accuracy and less standard deviation prediction results than classical echo state networks.