Shaoyong Hua

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In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed(More)
In the paper, we first address the uniformly exponential stability (UES) problem of a group of distributed cooperative adaptive systems in a general framework. Inspired by consensus theory, distributed cooperative adaptive laws are proposed to estimate the unknown parameters of these systems. It is shown that not only the whole closed-loop system is stable,(More)
This paper considers the problem of cooperative adaptive identification for a class of nonlinear systems via neural networks. The proposed adaptive laws of neural network weights are distributed, and the interconnection topologies are established among identification models in order to share their data on-line. It is proved that if the interconnection(More)
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