34 research outputs found
Delay-Dependent Guaranteed Cost Controller Design for Uncertain Neural Networks with Interval Time-Varying Delay
This paper studies the problem of guaranteed cost control for a class of uncertain
delayed neural networks. The time delay is a continuous function belonging to a given
interval but not necessary to be differentiable. A cost function is considered as a
nonlinear performance measure for the closed-loop system. The stabilizing controllers
to be designed must satisfy some exponential stability constraints on the closed-loop
poles. By constructing a set of augmented Lyapunov-Krasovskii functionals combined
with Newton-Leibniz formula, a guaranteed cost controller is designed via memoryless
state feedback control, and new sufficient conditions for the existence of the guaranteed
cost state feedback for the system are given in terms of linear matrix inequalities
(LMIs). Numerical examples are given to illustrate the effectiveness of the obtained
result
A constructive way to design a switching rule and switching regions to mean square exponential stability of switched stochastic systems with non-differentiable and interval time-varying delay
Mean square robust stability of stochastic switched discrete-time systems with convex polytopic uncertainties
Delay-dependent optimal guaranteed cost control of stochastic neural networks with interval nondifferentiable time-varying delays
Anti-bacterial activity of inorganic nanomaterials and their antimicrobial peptide conjugates against resistant and non-resistant pathogens
This review details the antimicrobial applications of inorganic nanomaterials of mostly metallic form, and the augmentation of activity by surface conjugation of peptide ligands. The review is subdivided into three main sections, of which the first describes the antimicrobial activity of inorganic nanomaterials against gram-positive, gram-negative and multidrug-resistant bacterial strains. The second section highlights the range of antimicrobial peptides and the drug resistance strategies employed by bacterial species to counter lethality. The final part discusses the role of antimicrobial peptide-decorated inorganic nanomaterials in the fight against bacterial strains that show resistance. General strategies for the preparation of antimicrobial peptides and their conjugation to nanomaterials are discussed, emphasizing the use of elemental and metallic oxide nanomaterials. Importantly, the permeation of antimicrobial peptides through the bacterial membrane is shown to aid the delivery of nanomaterials into bacterial cells. By judicious use of targeting ligands, the nanomaterial becomes able to differentiate between bacterial and mammalian cells and, thus, reduce side effects. Moreover, peptide conjugation to the surface of a nanomaterial will alter surface chemistry in ways that lead to reduction in toxicity and improvements in biocompatibility
Geometrically (Q, S, R)‐Incremental Dissipativity and Incremental Stability for Switched Time‐Varying Nonlinear Discrete‐Time Systems
Stability analysis and robust tracking control for a class of switched nonlinear systems with uncertain input delay
Synchronization of Fractional Order Uncertain BAM Competitive Neural Networks
This article examines the drive-response synchronization of a class of fractional order uncertain BAM (Bidirectional Associative Memory) competitive neural networks. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve global asymptotic stability of fractional order uncertain BAM competitive neural networks. This novel approach is based on the linear matrix inequality (LMI) technique and the derived conditions are easy to verify via the LMI toolbox. Moreover, numerical examples are presented to show the feasibility and effectiveness of the theoretical results
Synchronization of Fractional Order Uncertain BAM Competitive Neural Networks
This article examines the drive-response synchronization of a class of fractional order uncertain BAM (Bidirectional Associative Memory) competitive neural networks. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve global asymptotic stability of fractional order uncertain BAM competitive neural networks. This novel approach is based on the linear matrix inequality (LMI) technique and the derived conditions are easy to verify via the LMI toolbox. Moreover, numerical examples are presented to show the feasibility and effectiveness of the theoretical results