Stability Analysis for Delayed Neural Networks With an Improved General Free-Matrix-Based Integral Inequality.
basic_science · Level V
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- Record sourced from PubMed, PMID 31034424.
- Also identified by DOI 10.1109/TNNLS.2019.2909350.
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Abstract
This paper revisits the problem of stability analysis for neural networks with a time-varying delay. An improved general free-matrix-based (FMB) integral inequality is proposed with an undetermined number m . Compared with the conventional FMB ones, the improved inequality involves a much smaller number of free matrix variables. In particular, the improved FMB integral inequality is expressed in a concrete form for any value of m . By employing the new inequality with a properly constructed Lyapunov-Krasovskii functional, a new stability condition is derived for neural networks with a time-varying delay. Two commonly used numerical examples are given to show strong competitiveness of the proposed approach in both the conservatism and computation burdens.