Non-fragile state estimation for fractional-order delayed memristive BAM neural networks.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 31446237.
- Also identified by DOI 10.1016/j.neunet.2019.08.003.
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Abstract
This paper deals with the non-fragile state estimation problem for a class of fractional-order memristive BAM neural networks (FMBAMNNs) with and without time delays for the first time. By means of a novel transformation and interval matrix approach, non-fragile estimators are designed and parameter mismatch problem is averted. Sufficient criteria are established to ascertain the error system is asymptotically stable based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs). Two examples are put forward to show the effectiveness of the obtained results.
Medical subject headings
- Algorithms
- Neural Networks, Computer