By Sanjuan Miguel A F
This overview quantity is composed an critical number of examine papers chronicling the hot growth in controlling chaos. the following, new theoretical principles, as experimental implementations of controlling chaos, are incorporated, whereas the functions contained during this quantity should be often called turbulent magnetized plasmas, chaotic neural networks, modeling urban site visitors and types of curiosity in celestial mechanics. contemporary development in Controlling Chaos presents an exceptional vast review of the subject material, and may be particularly worthwhile for graduate scholars, researchers and scientists operating within the parts of nonlinear dynamics, chaos and intricate structures. The authors, world-renowned scientists and well-known specialists within the box of controlling chaos, will supply readers via their examine works, a desirable perception into the state of the art expertise utilized in the growth in key strategies and ideas within the box of keep an eye on.
Read or Download Recent Progress in Controlling Chaos (Series on Stability, Vibration and Control of Systems, Series B) (Series on Stability, Vibration and Control of Systems: Series B) PDF
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Additional resources for Recent Progress in Controlling Chaos (Series on Stability, Vibration and Control of Systems, Series B) (Series on Stability, Vibration and Control of Systems: Series B)
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Adachi et al. calculated the eigenvalues of the Jacobian Matrix of the CNN and concluded that by using simple chaos control methods, the controlled CNN cannot be stabilized to a stored pattern, and a new control strategy must be developed for the CNN [36]. For the associative memory dynamics, Nakamura et al. [37], Kushibe et al. [38], He et al. [39]–[45], and Shrimali et al. [46]–[47] have proposed some chaos control methods for chaotic neural networks and investigated the controlled dynamics. In this chapter, we focus on several chaos control methods concerned with associative memory and the relation between the stable output and the initial state in the CNN.
65, pp. 3211–3214, 1990. [25] V. Petrov, V. Gaspar, J. Masere, and K. Showwalter, “Controlling chaos in the Belousov–Zhabotinsky reaction,” Nature, vol. 361, pp. 240–243, 1993. [26] R. W. Rollins, P. Parmanada, and P. Sherard, “Controlling chaos in highly dissipative system–a simple recursive algorithm,” Phys. Rev. E, vol. 47, pp. 780–783, 1993. [27] R. Roy, T. W. Murphy, T. D. Maier, and Z. Gills, “Dynamical control of a chaotic laser–experimental stabilization of globally coupled system,” Phys.
N, (1) where f (·, a) is a one-dimensional unimodal map with a control parameter a. xn is the value of the state variable at time n, and k represents an additional parameter for constant feedback. The chaotic dynamics can be converted to stable periodic one by tuning k externally [Gueron (1998)]. In what we propose here, we suppose that the dynamical system (see Eq. (1)) possesses a region in the parameter k, where chaotic behaviors are observed with a great number of intermingled periodic windows.