Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
Neural-Memory Based Control of Micro Air Vehicles (MAVs) with Flapping Wings
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A neural-fuzzy sliding mode observer for robust fault diagnosis
ACC'09 Proceedings of the 2009 conference on American Control Conference
An observer-based neural networks control scheme for nonlinear systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Real-time implementation of Chebyshev neural network observer for twin rotor control system
Expert Systems with Applications: An International Journal
Automatica (Journal of IFAC)
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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A stable neural network (NN)-based observer for general multivariable nonlinear systems is presented in this paper. Unlike most previous neural network observers, the proposed observer uses a nonlinear-in-parameters neural network (NLPNN). Therefore, it can be applied to systems with higher degrees of nonlinearity without any a priori knowledge about system dynamics. The learning rule for the neural network is a novel approach based on the modified backpropagation (BP) algorithm. An e-modification term is added to guarantee robustness of the observer. No strictly positive real (SPR) or any other strong assumption is imposed on the proposed approach. The stability of the recurrent neural network observer is shown by Lyapunov's direct method. Simulation results for a flexible-joint manipulator are presented to demonstrate the enhanced performance achieved by utilizing the proposed neural network observer.