This paper deals with the L2-L∞ filtering problem for continuous-time Takagi-Sugeno fuzzy delayed Hopfield neural networks based on Wirtinger-type inequalities. A new set of delay-dependent conditions is established to estimate the state variables of fuzzy neural networks through the observed input and output variables. This ensures that the state estimation error system is asymptotically stable with a guaranteed L2-L∞ performance. The presented criterion is formulated in terms of linear matrix inequalities (LMIs). An example with simulation results is given to illustrate the effectiveness of the proposed fuzzy neural state estimator.
Bibliographical noteFunding Information:
This work was supported partially by the Australian Research Council ( DP140102180 , LP140100471 ) and the 111 Project ( B12018 ), partially by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF- 2014R1A1A1006101 ), partially by the Industrial Strategic Technology Development Program (No. 10041779 , Development of Energy Demand Response System for Smart Home) funded by the Ministry of Trade, Industry and Energy (MI, Korea), partially by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2013R1A1A2060663 ), and partially by the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) and Honam Institute for Regional Program Evaluation through the Leading Industry Development for Economic Region.
© 2014 Elsevier B.V.
- L-L filtering
- Linear matrix inequality (LMI)
- Takagi-Sugeno fuzzy Hopfield neural network
- Wirtinger-type inequality
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence