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Design and performance analysis of deterministic learning of sampled-data nonlinear systems

Science China(Information Sciences) 2014年第3期57卷 204-221页

作者： YUAN ChengZhi WANG Cong College of Automation and the Center for Control and Optimization South China University of Technology

In this paper,we extend the deterministic learning theory to sampled-data nonlinear *** on the Euler approximate model,the adaptive neural network identifier with a normalized learning algorithm is *** is proven that by properly setting the sampling period,the overall system can be guaranteed to be stable and partial neural network weights can exponentially converge to their optimal values under the satisfaction of the partial persistent excitation(PE)***,locally accurate learning of the nonlinear dynamics can be achieved,and the knowledge can be represented by using constant-weight neural ***,we present a performance analysis for the learning algorithm by developing explicit bounds on the learning rate and *** factors that influence learning,including the PE level,the learning gain,and the sampling period,are *** studies are included to demonstrate the efectiveness of the approach.

关键词： neural network identification deterministic learning persistent excitation sampled-data systems radial basis function networks(RBFNs)

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Relationship between Levels of Persistent Excitation,Architectures of neural networks and Deterministic Learning Performance

Relationship between Levels of Persistent Excitation,Archite...

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第36届中国控制会议

作者： Tongjia Zheng Cong Wang School of Automation Science and Technology South China University of Technology Guangdong Key Laboratory of Biomedical Engineering South China University of Technology

ISBN: (纸本)9781538629185

Based on the concept of persistent excitation（PE）, a deterministic learning algorithm is proposed for neural network（NN）-based identification of nonlinear systems recently. This paper investigates the quantitative relationship between the PE levels（including the level of excitation）, the architectures of NNs and the convergence properties of deterministic learning,which is motivated by a practical problem of how to construct the NNs in order to guarantee sufficient level of excitation and identification accuracy for specific system trajectories. The results on PE levels are utilized to analyze the deterministic learning performance. It is proven that by increasing the density of NN centers, the approximation capabilities of NNs increase but the level of excitation decreases, which means that a trade-off exists in the convergence accuracy when adjusting NN architectures.

关键词： PE condition level of excitation architecture of neural networks neural network identification deterministic learning

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