Reachable Set Estimation for Memristive Complex-Valued Neural Networks With Disturbances
Song Zhu, Yu Gao, Yuxin Hou, Chunyu Yang
Abstract
This brief focuses on reachable set estimation for memristive complex-valued neural networks (MCVNNs) with disturbances. Based on algebraic calculation and Gronwall-Bellman inequality, the states of MCVNNs with bounded input disturbances converge within a sphere. From this, the convergence speed is also obtained. In addition, an observer for MCVNNs is designed. Two illustrative simulations are also given to show the effectiveness of the obtained conclusions.
Topics & Concepts
Observer (physics)Bounded functionArtificial neural networkConvergence (economics)Set (abstract data type)Control theory (sociology)Computer scienceAlgebraic numberMathematicsApplied mathematicsArtificial intelligenceMathematical analysisControl (management)EconomicsPhysicsProgramming languageQuantum mechanicsEconomic growthNeural Networks Stability and SynchronizationNeural Networks and ApplicationsAdvanced Memory and Neural Computing