Applications of Chaos and Nonlinear Dynamics in Science and by Bingo Wing-Kuen Ling (auth.), Santo Banerjee, Lamberto

Applications of Chaos and Nonlinear Dynamics in Science and by Bingo Wing-Kuen Ling (auth.), Santo Banerjee, Lamberto

By Bingo Wing-Kuen Ling (auth.), Santo Banerjee, Lamberto Rondoni, Mala Mitra (eds.)

Chaos and nonlinear dynamics at first constructed as a brand new emergent box with its starting place in physics and utilized arithmetic. The hugely commonly used, interdisciplinary caliber of the insights won within the previous couple of a long time has spawned myriad purposes in just about all branches of technology and technology—and even well past. at any place the quantitative modeling and research of complicated, nonlinear phenomena are required, chaos thought and its equipment can play a key function.

This moment quantity concentrates on reviewing additional correct, modern functions of chaotic nonlinear platforms as they observe to a number of the state of the art branches of engineering. This encompasses, yet isn't constrained to, themes similar to the unfold of epidemics; digital circuits; chaos regulate in mechanical units; safe communique; and electronic watermarking.

Featuring contributions from energetic and top examine teams, this assortment is perfect either as a reference paintings and as a ‘recipe publication’ filled with attempted and proven, profitable engineering applications.

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Applications of Chaos and Nonlinear Dynamics in Science and Engineering - Vol. K. Ghosh et al. 2 A sample from KB showing the variables and assignments Sl no. Fuzzy variable Subvariable Weightage 1. 7 36 –45 26 –35 16 –25 < 16 2. 4 3. 3 Function based rating (Signum function) 9 7 5 3 1 (Gaussian function) 9 8 6 4 3 1 (Signum function) 9 7 5 3 1 attributes such as geology, structure, surface cover, vegetation cover, the variability of terrain parameters and, distribution of landslides of an area.

J. Geogr. Inform. Sci. 14(2), 173–184 (2000). html 20. : GIS-based multicriteria decision analysis: a survey of the literature. Int. J. Geogr. Inform. Sci. 20(7), 703–726 (2006) 21. : The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2), 15–31 (2005) 22. : GIS-based landslide susceptibility for Arsin- Yomra (Trabzon, North Turkey) region. Environ. Geol. 51(8), 1377–1387 (2007) 23. : A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in penialpine Slovenia.

We choose the output matrices as ! À0:8 0:1 Cð1Þ ¼ ; 0:3 0:5 Cð2Þ ¼ 0:1 0:7 0:4 À0:2 ! R. 5 0 2 4 6 8 Time[s] 10 12 14 16 Fig. 2 Synchronization errors It is clear that the matrices Qð1Þ and Qð2Þ should be zeros. 24), the LMI variables are obtained as follows: 4:3535 À1:4945 PQ 1 ¼ P C1 ! À1:4945 ; 2:2963 1:4261 ¼ À0:1222 À0:1222 0:3085 4:0501 1:9884 ! 1:9884 ; 7:7804 PQ 2 ¼ P C2 0:5392 ¼ 0:0126 YC1 ¼ ½À1:3591 0:0126 1:6597 ! 1:0997Š; YC2 ¼ ½À0:7287 À 0:2845Š The control gains are calculated as Kð1Þ ¼ ½À0:6703 3:2990Š; Kð2Þ ¼ ½À1:3478 À 0:1612Š: 3 An Output Feedback Control Design 53 Responses of the two synchronization error signals of the closed-loop system are depicted in Fig.

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