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This paper presents a novel identification method of induction motor via Dynamic Neural Networks with two-time scales using sliding mode learning. Due to the fast adaptation and superb learning capability, Dynamic Neural Networks with two-time scales using sliding mode learning are used to identify the induction motor including the aspects of fast and slow phenomenon. The sliding mode technique and...
In this paper, an new on-line identification algorithm with dead-zone function is proposed for nonlinear systems identification via dynamic neural networks with different time-scales including the aspects of fast and slow phenomenon. The main contribution of the paper is that the Lyapunov function and singularly perturbed techniques are used to develop the on-line update laws for both dynamic neural...
In this paper, a new genetic algorithm (GA) is developed to optimize the architecture of a recurrent artificial neural network (RANN) with multiple hidden layers. A new direct matrix mapping encoding (DMME) method is proposed to efficiently and effectively represent the architecture of a neural network. A modified back-propagation (BP) algorithm is utilized to tune the weights and other parameters...
Quite successfully adaptive control strategies have been applied to dynamical systems subject to dead-zone nonlinearities. However, adaptive tracking of systems with non-symmetric dead-zones characteristics has not been fully discussed with minimal knowledge of the dead-zones parameters. First, we develop a new adaptive control algorithm for systems involving unknown symmetric dead-zones control inputs...
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