An approach to learning control surfaces by connectionist systems
Vision, brain, and cooperative computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Performance Evaluation of Evolutionarily Created Neural Network Topologies
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
An effective learning of neural network by using RFBP learning algorithm
Information Sciences—Informatics and Computer Science: An International Journal
A heuristic fault tolerant control for nonlinear MIMO systems using neural networks
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Expert Systems with Applications: An International Journal
Robust stability of nonlinear neural-network modeled systems
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
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Artificial neural networks-especially those using the error back propagation algorithm-are capable of learning to control an unknown plant by autonomously extracting the necessary information from the plant. Following the approach of Psaltis, Sideris, and Yamamura, and Saerens and Soquet, a control architecture based on error back propagation has been developed and trained to control a third order linear and time invariant plant with dead-time Simulation results show that the network is able to invert the plant's behaviour and characteristics, thus learning to control the plant accurately. The time to reach the desired outputs of the plant decreases while learning. It is accelerated by local adaptation of the learning rate.