Synchronization of Timescale-Type Nonautonomous Neural Networks With Proportional Delays
Qiang Xiao, Tingwen Huang, Zhigang Zeng
Abstract
Synchronization of a class of drive-response timescale-type nonautonomous proportional-delayed neural networks (TNPNNs) is addressed in this article. The key technique to cope with the proportional term is using comparison principle. By timescale theory, inequality technique, and comparison principle, criteria of synchronization are obtained. The method used in this article is effective to cope with TNPNNs and it is a direct approach as well by eliminating the conventional exponential transformation. The obtained results are verified with three examples.
Topics & Concepts
Synchronization (alternating current)Control theory (sociology)Artificial neural networkTransformation (genetics)Class (philosophy)Type (biology)Computer scienceKey (lock)MathematicsApplied mathematicsArtificial intelligenceTopology (electrical circuits)Control (management)CombinatoricsChemistryEcologyGeneComputer securityBiologyBiochemistryNeural Networks Stability and Synchronizationstochastic dynamics and bifurcationNonlinear Dynamics and Pattern Formation