These three concepts are related to the concept of dynamic modality and they regard the long-term behaviour of a dynamical system. Attractors are trajectories of the system state variable that emerge in the long-term, with relative independence with respect to the exact values of the initial conditions. These long-term trajectories can be either a point in the state space (a static asymptotic behaviour), named fixed-point, a cyclic pattern (named limit cycle), or even a chaotic trajectory. Repellers correspond, qualitatively speaking, to the opposite behaviour of attractors: given a fixed-point or a cyclic trajectory of a dynamic system, they are called repeller-type trajectories if small perturbations can make the system evolve to trajectories that are far from the original one.
Published in Chapter:
Chaotic Neural Networks
Emilio Del-Moral-Hernandez (University of São Paulo, Brazil)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch043
Abstract
Artificial Neural Networks have proven, along the last four decades, to be an important tool for modelling of the functional structures of the nervous system, as well as for the modelling of non-linear and adaptive systems in general, both biological and non biological (Haykin, 1999). They also became a powerful biologically inspired general computing framework, particularly important for solving non-linear problems with reduced formalization and structure. At the same time, methods from the area of complex systems and non-linear dynamics have shown to be useful in the understanding of phenomena in brain activity and nervous system activity in general (Freeman, 1992; Kelso, 1995). Joining these two areas, the development of artificial neural networks employing rich dynamics is a growing subject in both arenas, theory and practice. In particular, model neurons with rich bifurcation and chaotic dynamics have been developed in recent decades, for the modelling of complex phenomena in biology as well as for the application in neuro-like computing. Some models that deserve attention in this context are those developed by Kazuyuki Aihara (1990), Nagumo and Sato (1972), Walter Freeman (1992), K. Kaneko (2001), and Nabil Farhat (1994), among others. The following topics develop the subject of Chaotic Neural Networks, presenting several of the important models of this class and briefly discussing associated tools of analysis and typical target applications.