Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Decentralized adaptive recurrent neural control structure
Engineering Applications of Artificial Intelligence
Discrete-time decentralized neural block controller for a five DOF robot manipulator
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Recursive Bayesian recurrent neural networks for time-series modeling
IEEE Transactions on Neural Networks
Output feedback control of a quadrotor UAV using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A discrete-time neural network for optimization problems with hybrid constraints
IEEE Transactions on Neural Networks
Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function
IEEE Transactions on Neural Networks
Identification of finite state automata with a class of recurrent neural networks
IEEE Transactions on Neural Networks
Neural network learning without backpropagation
IEEE Transactions on Neural Networks
Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance
IEEE Transactions on Neural Networks
Decentralized adaptive fuzzy control of robot manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discrete-time inverse optimal neural control for synchronous generators
Engineering Applications of Artificial Intelligence
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A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.