Nonlinear prediction, filtering, and control of chemical systems from time series

Chaos

Volumn 7, Issue 4, 1997, Pages 614-620

  Petrov, Valery ^a   Showalter, Kenneth ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b WEST VIRGINIA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0040453559     PISSN: 10541500     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.166261     Document Type: Article

References (23)

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3. For recent reviews, see: T. Shinbrot, C. Grebogi, E. Ott, and J. A. Yorke, "Using small perturbations to control chaos," Nature (London) 363, 411 (1993); W. L. Ditto and L. Pecora, "Mastering chaos," Sci. Am. 269, 78 (1993); E. Ott and M. Spano, "Controlling chaos," Phys. Today 48, 34 (1995).
4. For recent reviews, see: T. Shinbrot, C. Grebogi, E. Ott, and J. A. Yorke, "Using small perturbations to control chaos," Nature (London) 363, 411 (1993); W. L. Ditto and L. Pecora, "Mastering chaos," Sci. Am. 269, 78 (1993); E. Ott and M. Spano, "Controlling chaos," Phys. Today 48, 34 (1995).
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18. Time-delayed readings of the observable were supplied to an input layer consisting of four linear neurons. The second layer consisted of 100 sigmoidal neurons and the third, output layer consisted of one linear neuron. The network was trained using back-propagation and then applied for the time-series prediction.
19. The number of points used in the reconstruction of F was the same for Figs. 3 and 5.
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