Nonlinear Chaotic Processing Model
This work addresses the problem of generating complex chaotic maps for researchers in chaos theory and related fields, but it appears incremental as it builds on existing chaotic maps.
The paper tackled the challenge of designing chaotic maps with complex dynamics by introducing the nonlinear chaotic processing (NCP) model, which uses six basic nonlinear operations to generate new chaotic maps from seed maps, and experimental results showed that four generated maps had more complex chaotic behaviors than existing ones.
Designing chaotic maps with complex dynamics is a challenging topic. This paper introduces the nonlinear chaotic processing (NCP) model, which contains six basic nonlinear operations. Each operation is a general framework that can use existing chaotic maps as seed maps to generate a huge number of new chaotic maps. The proposed NCP model can be easily extended by introducing new nonlinear operations or by arbitrarily combining existing ones. The properties and chaotic behaviors of the NCP model are investigated. To show its effectiveness and usability, as examples, we provide four new chaotic maps generated by the NCP model and evaluate their chaotic performance using Lyapunov exponent, Shannon entropy, correlation dimension and initial state sensitivity. The experimental results show that these chaotic maps have more complex chaotic behaviors than existing ones.