Input-to-state $\mathcal{H}_\infty$ learning of recurrent neural networks with delay and disturbance

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Cites Work

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Stochastic input-to-state stability of switched stochastic nonlinear systems
Exponential stabilization of delayed recurrent neural networks: A state estimation based approach
Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay
Weighted $\mathcal{H}_\infty$ consensus design for stochastic multi-agent systems subject to external disturbances and ADT switching topologies
Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms.
Generalized exponential input-to-state stability of nonlinear systems with time delay
Finite-time $H_\infty$ dynamic output feedback control for nonlinear impulsive switched systems
$H_\infty$ controller design of networked control systems with a new quantization structure
A waiting-time-based event-triggered scheme for stabilization of complex-valued neural networks
Resilient fault-tolerant anti-synchronization for stochastic delayed reaction-diffusion neural networks with semi-Markov jump parameters
Resilient input-to-state stable filter design for nonlinear time-delay systems
Extended dissipative learning of time-delay recurrent neural networks
Input-to-state stability and integral input-to-state stability of nonlinear impulsive systems with delays
Critical delays and polynomial eigenvalue problems
${\mathcal H}_{\infty}$ WEIGHT LEARNING ALGORITHM OF RECURRENT NEURAL NETWORKS WITH TIME-DELAY
Low‐complexity ISS state estimation approach with bounded disturbances
Trace bounds on the solution of the algebraic matrix Riccati and Lyapunov equation
Smooth stabilization implies coprime factorization
Quantized passive filtering for switched delayed neural networks
Dynamic Systems with Time Delays: Stability and Control
Output‐feedback H_∞ quadratic tracking control of linear systems using reinforcement learning
Mittag‐Leffler state estimator design and synchronization analysis for fractional‐order BAM neural networks with time delays
Neurons with graded response have collective computational properties like those of two-state neurons.
Input-to-state stability for discrete-time nonlinear systems

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Input-to-state \(\mathcal{H}_\infty\) learning of recurrent neural networks with delay and disturbance