Recent Progress in Controlling Chaos (Series on Stability, by Sanjuan Miguel A F

By Sanjuan Miguel A F

This assessment quantity is composed an necessary choice of examine papers chronicling the hot growth in controlling chaos. right here, new theoretical principles, as experimental implementations of controlling chaos, are incorporated, whereas the purposes contained during this quantity should be known as turbulent magnetized plasmas, chaotic neural networks, modeling urban site visitors and types of curiosity in celestial mechanics. contemporary development in Controlling Chaos presents a superb huge evaluate of the subject material, and may be particularly important for graduate scholars, researchers and scientists operating within the parts of nonlinear dynamics, chaos and intricate platforms. The authors, world-renowned scientists and famous specialists within the box of controlling chaos, will provide readers via their learn works, a desirable perception into the state of the art know-how utilized in the growth in key suggestions and ideas within the box of keep watch over.

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Additional info 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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This indicates that the controlled CNN can be feasibly applied to information processing after controlling the chaos. The dynamics of the controlled CNN is usually periodic, and the period of the output sequence depends on the initial state. These characteristics are similar to those of the olfactory bulb in which the dynamics November 25, 2009 9:55 World Scientific Review Volume - 9in x 6in Controlling Chaos in a Chaotic Neural Network RecentProgress-chapter2 41 becomes ordered and nearly periodic when the brain identifies a familiar scent [5].

L. Ditton, and J. Weiss, “Controlling cardiac chaos,” Science, vol. 257, pp. 1230–1235, 1992. [29] S. J. Schiff, K. Jerger, D. H. Duong, T. Chang, M. L. Spano, and W. L. Ditto, “Controlling chaos in the brain,” Nature, vol. 370, pp. 615–620, 1994. [30] X. Gong, F. Li, and H. Zhang, “On the control of chaotic neurons,” Chaos, Solitons and Fractals, vol. 7, pp. 1397–1409, 1996. [31] S. Sinha, “Controlled transition from chaos to periodic oscillations in a neural network model,” Physical A, vol. 224, pp.

The CF method, indeed, shares with OGY and DFC the great advantage that no a priori information on the original system is required, and has been widely applied [Parekh et al. (1998); Sole et al. (1999)]. Furthermore, as we will see momentarily, the proposed technique tunes adaptively and automatically the CF’s additional parameter, and therefore it has the further advantage of not even requiring the knowledge of the bifurcation analysis of the new system with respect to the additional parameter.

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