Robust State Estimation for Delayed Neural Networks with Stochastic Parameter UncertaintiesReport as inadecuate




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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 948391, 18 pages -

Research Article

School of Electrical Engineering, Chungbuk National University, 52 Naesudong-ro, Cheongju 361-763, Republic of Korea

Nonlinear Dynamics Group, Department of Electrical Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 712-749, Republic of Korea

School of Electronic Engineering, Daegu University, Gyeongsan 712-714, Republic of Korea

Department of Biomedical Engineering, School of Medicine, Chungbuk National University, 52 Naesudong-ro, Cheongju 361-763, Republic of Korea

Received 6 June 2014; Accepted 28 July 2014

Academic Editor: Quanxin Zhu

Copyright © 2015 M. J. Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper considers the problem of delay-dependent state estimation for neural networks with time-varying delays and stochastic parameter uncertainties. It is assumed that the parameter uncertainties are affected by the environment which is changed with randomly real situation, and its stochastic information such as mean and variance is utilized in the proposed method. By constructing a newly augmentedLyapunov-Krasovskii functional, a designing method of estimator for neural networks is introduced with the framework of linear matrix inequalities LMIs and a neural networks model with stochastic parameter uncertainties which have not been introduced yet. Two numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed idea.





Author: M. J. Park, O. M. Kwon, Ju H. Park, S. M. Lee, and E. J. Cha

Source: https://www.hindawi.com/



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