Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks for control
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Approximate solutions to the time-invariant Hamilton-Jacobi-Bellman equation
Journal of Optimization Theory and Applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neural dynamic optimization for control systems. I. Background
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
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Abstract: This work is aimed at looking into the determination of optimal neuro-feedback control for discrete time nonlinear systems. The basic idea consists in the use of two coupled neural networks to approximate the solution of the Hamilton-Jacobi-Bellman equation (HJB) and to obtain a robust feedback closed-loop control law. The used learning algorithm is a modified version of the backpropagation one. As an illustration, a numerical nonlinear discrete time example is considered. Simulation results show the effectiveness of the proposed method.