Currently, low-dimensional chaotic maps have many disadvantages, such as narrow chaotic
regions, numerous cycle windows, and weak chaotic representations. Based on this issue …

High-dimensional (HD) chaotic map has wide applications in various research fields such as
neural networks and secure communication. Designing HD chaotic maps with expected …

Multivariate chaotic time series prediction is a challenging task, especially when multiple
variables are predicted simultaneously. For multiple related prediction tasks typically require …

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 …

Multivariate time series (MTS) prediction has been studied broadly, which is widely applied
in real-world applications. Recently, transformer-based methods have shown the potential in …

Fuzzy neural network (FNN) is regarded as a prominent approach in application of time-
series modeling. With the capability of fuzzy reasoning, FNN can capture temporal patterns …

Cooperative coevolutionary algorithms have been developed for large-scale dynamic
optimization problems via divide-and-conquer mechanisms. Interacting decision variables …

The Lyapunov exponent serves as a measure of the average divergence or convergence
between chaotic trajectories from the perspective of Lyapunov exponents (LEs). Chaotic …

Feature mining is a challenging work in the field of multivariate time series (MTS) data
mining. Traditional methods suffer from three major issues. 1) Learned shapelets may …

Secure communication techniques can protect data confidentiality during transmission
through public channels. Chaotic systems are commonly used in secure communication due …