An Arcak-type state estimation design for time-delayed static neural networks with leakage term based on unified criteria. (October 2018) - Manivannan, R.; Panda, S.; Chong, Kil To; Cao, Jinde
Abstract: The issue of unified dissipativity-based Arcak-type state estimator design for delayed static neural networks (SNNs) with leakage term and noise distraction was considered here. An Arcak-type state observer, which is compact than the usually used Luenberger-type state estimator, is selected to implement the subject of a unified dissipativity performance of SNNs. This paper primarily concentrates on the issue of Arcak-type state estimator of delayed SNNs involving leakage delay. The first attempt is made to tackle the Arcak-type state estimator of SNNs with time delay in leakage term in this paper based on the unified criteria, by constructing a novel Lyapunov functional together with newly improved integral inequalities. As a result, a novel unified state estimation criterion is launched in the form of linear matrix inequalities (LMIs) and put forward to justify the dynamics of error system is extended dissipative with the influence of leakage term and estimator gain matrices K ¯ 1 and K ¯ 2 . Finally, an interesting simulation study is ultimately explored to show the performance of the established unified dissipativity-based theoretical results, in which, comparison results are also made together with recent works as a special case.