Uniform Stability of Complex-Valued Neural Networks of Fractional Order With Linear Impulses and Fixed Time Delays
Hui Li, Yonggui Kao, Haibo Bao, YangQuan Chen
Abstract
As a generation of the real-valued neural network (RVNN), complex-valued neural network (CVNN) is based on the complex-valued (CV) parameters and variables. The fractional-order (FO) CVNN with linear impulses and fixed time delays is discussed. By using the sign function, the Banach fixed point theorem, and two classes of activation functions, some criteria of uniform stability for the solution and existence and uniqueness for equilibrium solution are derived. Finally, three experimental simulations are presented to illustrate the correctness and effectiveness of the obtained results.
Topics & Concepts
UniquenessFixed pointCorrectnessArtificial neural networkBanach fixed-point theoremFixed-point theoremMathematicsStability (learning theory)Sign functionApplied mathematicsEquilibrium pointSign (mathematics)Order (exchange)Function (biology)Mittag-Leffler functionControl theory (sociology)Fractional calculusComputer scienceMathematical analysisAlgorithmDifferential equationControl (management)Artificial intelligenceMachine learningFinanceEvolutionary biologyEconomicsBiologyNeural Networks Stability and SynchronizationNeural Networks and ApplicationsFractional Differential Equations Solutions