Global exponential stability of delayed inertial competitive neural networks
Min Shi, Juan Guo, Xianwen Fang, Chuangxia Huang
Abstract
Abstract In this paper, the exponential stability for a class of delayed competitive neural networks is studied. By applying the inequality technique and non-reduced-order approach, some novel and useful criteria of global exponential stability for the addressed network model are established. Moreover, a numerical example is presented to show the feasibility and effectiveness of the theoretical results.
Topics & Concepts
Exponential stabilityOrdinary differential equationMathematicsArtificial neural networkExponential functionStability (learning theory)Inertial frame of referenceControl theory (sociology)Applied mathematicsPartial differential equationExponential growthMathematical optimizationComputer scienceDifferential equationMathematical analysisArtificial intelligenceNonlinear systemMachine learningPhysicsQuantum mechanicsControl (management)Neural Networks Stability and SynchronizationNeural Networks and Applicationsstochastic dynamics and bifurcation