Lyapunov exponents (LEs) characterize the average exponential rate of convergence or
divergence between adjacent orbits in phase space. Thus, the number of positive LEs can …

Phase space reconstruction is crucial for predicting chaotic hydrological time series.
However, traditional multivariate phase space reconstruction methods, such as high …

Rolling bearings are key components of rotary machines. To ensure early effective fault
diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational …

In this paper, a novel method (Hybrid–ELM–PLSR) is proposed based on hybrid variable
selection algorithm and improved extreme learning machine (ELM) for multivariate chaotic …

Global efforts to build sustainable e-commerce ecosystems through various prediction tools
have suffered due to uncertainty in politics, the economy, and the environment. This paper …

Based on the theorem of eigenvalue estimation, this paper proposes a construction
algorithm for an N-dimensional discrete non-degenerate complex domain chaotic system …

为提高混沌时间序列的预测精度, 提出一种基于混合神经网络和注意力机制的预测模型(Att-
CNNLSTM), 首先对混沌时间序列进行相空间重构和数据归一化, 然后利用卷积神经网络(CNN) …

In the process of online prediction of multivariable non-stationary time series by kernel
extreme learning machine (KELM), the dynamic characteristics of the system which are …

针对时间序列预测, 在单隐层前馈神经网络的基础上, 基于进化计算的优化策略,
提出了一种优化的核极限学**机(optimized kernel extreme learning machine, O-KELM) 方法 …

In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often
submerged in strong chaotic noise, and the targeted signal data are difficult to recover …