A neural network (400) is trained with a general set of data to function as a general model of a machine (10) or process with local condition inputs set equal to zero. The network (400) is then retrained or receives additional training on an extentd data set containing the general set...
Using conventional control theory for design and synthesis of the controller seems to be almost impossible when we are dealing with such a nonlinear system. In this paper an artificial neural network based control system of a robot for underwater ship hull cleaning is proposed. The underwater ...
Neural network-based servo control system and methoddoi:CN102053628 AThe invention aims to improve the control accuracy of a servo system and provides a neural network model reference adaptive control method applied to the servo system. The nonlinearity of the servo system is effectively compensated,...
Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The pro... Chin-Teng,Lin,Lee,... - 《Fuzzy Systems IEEE...
For a class of MIMO sampled-data nonlinear systems with unknown dynamic nonlinearities, a stable neural-network (NN)-based adaptive control approach which ... F Sun,Z Sun,PY Woo - 《Neural Networks IEEE Transactions on》 被引量: 204发表: 1998年 Neural Network Adaptive Control for a class ...
Many BEL-based network controllers produce good performances in controlling dynamic systems [32], [33], [34], [35]. The control performance is expected to be greatly improved if the fast responsive ability can be integrated with the excellent nonlinear approximation ability. The control system is...
His current research interests include adaptive dynamic programming, neural network; adaptive control, optimal control,References (30) Q. Wei et al. Data-based optimal control for discrete-time zero-sum games of 2-D systems using adaptive critic designs ACTA Autom. Sin. (2009) D. Liu et al....
Genetic-based neural network control for chaotic system混沌系统的遗传神经网络控制 A novel genetic-based neural network control for chaos is presented. The method proposed has been successfully applied to control two simulated chaotic sys... Wang YaoNan,Tan Wen,王耀南,... - 《物理学报》 被引量:...
This study develops a neural-network-based robust control scheme for steer-by-wire systems with uncertain dynamics. The proposed control consists of a nominal control and a nonsingular terminal sliding mode compensator where a radial basis function neural network (RBFNN) is adopted to adaptively lear...
In this paper, a neural-network-based optimal control scheme for a class of nonlinear discrete-time systems with control constraints is proposed. The iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to design the optimal co...