Abstract—In this paper, a new guaranteed performance state estimation problem for static neural networks with time- varying delay is investigated. A new Lyapunov-Krasovskii func- tional is introduced to improve the performance. Moreover, with the help of lower bound lemma, an upper-bound of a linear combination of positive functions weighted by the inverses of convex parameters is obtained. Two simulation examples are given to prove the effectiveness of the proposed theorem.
Index Terms—State estimation, static neural networks, H-infinite performance, reciprocally convex approach, Tim evarying delay.
Won Il Lee is with the Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784Republic of Korea (e-mail: firstname.lastname@example.org).
Poo Gyeon Park is with the Division of ITCE and Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH),Pohang, 790-784 Republic of Korea (e-mail: email@example.com).
Cite: Won Il Lee and Poo Gyeon Park, "New Guaranteed H∞ Performance State Estimation for Delayed Neural Networks," International Journal of Information and Electronics Engineering vol. 2, no. 6, pp. 923-927, 2012.