By Sundarapandian Vaidyanathan (auth.), David C. Wyld, Michal Wozniak, Nabendu Chaki, Natarajan Meghanathan, Dhinaharan Nagamalai (eds.)

This publication constitutes the complaints of the 1st foreign convention on Advances in Computing and data know-how, ACITY 2011, held in Chennai, India, in July 2011. The fifty five revised complete papers provided have been rigorously reviewed and chosen from various submissions. The papers characteristic major contributions to all significant fields of the pc technological know-how and data expertise in theoretical and sensible aspects.

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Additional resources for Advances in Computing and Information Technology: First International Conference, ACITY 2011, Chennai, India, July 15-17, 2011. Proceedings

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Approach is dependent on SPs • • • • SPs are not required. • Combining Backpropagati on network with SVM gives better results for classification. • • Training consumes a lot of time. Requires target values. 38 D. Parekh and Rekha Vig Table 3. No Approach Characteristic Advantages 1. 2. 3. B. Kim, J. Bae, I. Yoo, 2000 • Y. Liu, S. Yuan, X. Zhu, Y. Wang, W. Zhang and Y. Wang, 2002 • • • • 4. 5. Tico, 2002 C. Klimanee and D. Nguyen • • • • • 6. Rakesh,20 06 • • • Disadvantages Poincare Index is used to detect core.

Mech. 11, 1309–1315 (2003) 11. : Control of chaos using sampled-data feedback control. Internat. J. Bifurcat. Chaos. 9, 215–219 (1999) 12. : Controlling chaos. Phys. Rev. Lett. 64, 1196–1199 (1990) 13. : A novel criterion for delayed feedback control of time-delay chaotic systems. Chaos, Solit. Fract. 17, 709–716 (2003) 14. : Parameter identification and backstepping control of uncertain L¨u system. Chaos, Solit. Fract. 18, 721–729 (2003) 15. : Adaptive synchronization between two different chaotic dynamical systems.

The matrix Qf f is invertible. (11) 22 V. Sundarapandian and M. Kavitha Using (H3), the equation (11) becomes −1 −1 xf (k) = −Q−1 Γf u(k) f f Qf s xs (k) + Qf f (I − Λf ) (12) To simplify the notation, we define the matrices −1 −1 R = −Q−1 Γf f f Qf s and S = Qf f (I − Λf ) (13) Using (13), the equation (12) can be simplified as xf (k) = Rxs (k) + Su(k) (14) Substituting (14) into (3), we obtain the reduced-order model S2 of the given linear system S1 as xs (k + 1) = As xs (k) + Bs u(k) (15) y(k) = Cs xs (k) + Ds u(k) where the matrices As , Bs , Cs and Ds are defined by As = Ass + Asf R, Bs = Bs + Asf S, Cs = Cs + Cf R and Ds = Cf S (16) 3 Reduced Order Observer Design In this section, we state a new result that prescribes a simple procedure for estimating the dominant state of the given linear control system S1 that satisfies the assumptions (H1)-(H3).

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