Pages that link to "Item:Q1429837"

The following pages link to Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms. (Q1429837):

Displaying 30 items.

• Peak-to-peak exponential direct learning of continuous-time recurrent neural network models: a matrix inequality approach (Q395779) (← links)
• Discrete-time recurrent high order neural networks for nonlinear identification (Q609792) (← links)
• A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system (Q710447) (← links)
• A novel deniable authentication protocol using generalized ElGamal signature scheme (Q865908) (← links)
• A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks (Q881894) (← links)
• Self-adaptive vibration control of simply supported beam under a moving mass using self-recurrent wavelet neural networks via adaptive learning rates (Q904842) (← links)
• Reinforcement radial basis function neural networks with an adaptive annealing learning algorithm (Q905330) (← links)
• Dynamic system identification via recurrent multilayer perceptrons (Q1857037) (← links)
• Nonlinear system identification using neural networks trained with natural gradient descent (Q1886938) (← links)
• Mechanical system modelling using recurrent neural networks via quasi- Newton learning methods (Q1900577) (← links)
• Discrete Pseudo-SINR-balancing nonlinear recurrent system (Q1956062) (← links)
• Stability of gated recurrent unit neural networks: convex combination formulation approach (Q2026732) (← links)
• Stability analysis for discrete-time stochastic fuzzy neural networks with mixed delays (Q2298891) (← links)
• Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates (Q2372213) (← links)
• \(\mathcal L_{2}-\mathcal L_{\infty }\) nonlinear system identification via recurrent neural networks (Q2431028) (← links)
• Robust stability of recurrent neural networks with ISS learning algorithm (Q2434143) (← links)
• Recurrent neural network-based internal model control design for stable nonlinear systems (Q2673618) (← links)
• Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications (Q2730021) (← links)
• Editorial: some recent advances in learning and adaptation for uncertain feedback control systems (Q2795789) (← links)
• Neural Network On-Line Modeling and Controlling Method for Multi-Variable Control of Wastewater Treatment Processes (Q2930818) (← links)
• Identification of Discrete Event Systems Using the Compound Recurrent Neural Network: Extracting DEVS from Trained Network (Q3148237) (← links)
• Adaptive control of discrete-time nonlinear systems using recurrent neural networks (Q4297346) (← links)
• Stable sequential identification of continuous nonlinear dynamical systems by growing radial basis function networks (Q4714659) (← links)
• Identification of nonlinear discrete-time systems using raised-cosine radial basis function networks (Q4828701) (← links)
• Dynamic recurrent neural network for system identification and control (Q4851163) (← links)
• Online identifier–actor–critic algorithm for optimal control of nonlinear systems (Q5280130) (← links)
• Dissipativity analysis of memristive neural networks with time‐varying delays and randomly occurring uncertainties (Q5741657) (← links)
• Input-to-state stability for system identification with continuous-time Runge–Kutta neural networks (Q6040951) (← links)
• Input-to-state \(\mathcal{H}_\infty\) learning of recurrent neural networks with delay and disturbance (Q6494662) (← links)
• A recurrent neural network approach for magneto-hydro-dynamic flow of second-grade fluid with dissipation effect (Q6662040) (← links)