Reachable Set Estimation of Inertial Complex-Valued Memristive Neural Networks
Jiemei Zhao, Yi Shen, Leimin Wang, Liqi Yu
Abstract
This brief investigates the reachable set estimation (RSE) of inertial complex-valued memristive neural networks (ICVMNNs) with bounded disturbances. By taking into account the analysis method and inequality technique, an algebraic criterion of RES is established. To deal with the inertial terms in memristive neural networks, a nonreduced-order approach is adopted. Besides, the non-separation analysis method is applied to investigate complex-valued problems. Then, a complex-valued feedback control scheme is designed to ensure that the states of ICVMNNs converge to a bounded region. Eventually, a numerical example is provided to illustrate the effectiveness of the obtained theoretical result.
Topics & Concepts
Artificial neural networkComputer scienceInertial frame of referenceSet (abstract data type)EstimationArtificial intelligenceEngineeringPhysicsSystems engineeringProgramming languageQuantum mechanicsNeural Networks and ApplicationsTarget Tracking and Data Fusion in Sensor NetworksImage and Signal Denoising Methods