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We demonstrate our algorithm in several dynamical systems exhibiting both periodic and chaotic oscillations.
This paper analyzes local and global behavior of several dynamical systems which generalize some artificial neural network (ANN) semilinear models originally designed for principal component analysis (PCA) in the characterization of random vectors.
The significance of the hybrid system of differential equations lies in the fact that they include several dynamical systems as special cases.
Switched systems can be efficiently used to model many practical systems which are inherently multi-model in the sense that several dynamical systems are required to describe their behavior.
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This analogy has several facets: In both cases the probability distributions are naturally associated with their respective dynamical systems.
Many natural systems can be modeled with several types of dynamical systems, and it is of interest to study which properties of differential equations models carry over to certain types of difference equation models.
Several approaches yield stable dynamical systems but are also limited to specific dynamics that can potentially result in a poor reproduction performance.
Together with the theory of copula, the efficiency and accuracy of the proposed method are illustrated by the probabilistic lifetime modelling of several dynamical and discontinuous stochastic systems.
The notion of pseudo-orbits very often appears in several areas of the dynamical systems.
In Refs. [28, 29], Wu and Fu introduced the concept of complex projective synchronization based on Lyapunov stability theory, several typical chaotic complex dynamical systems are considered and the corresponding controllers are designed to achieve the complex projective synchronization.
Research Interests Natural Algorithmss, Dynamical Systems, Dynamic Networks, Computational Geometry, Discrepancy Theory.
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