T-S模糊神经网络系统同步的改进综合与分析结果

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI:10.1109/ICIST52614.2021.9440602

Wenqiang Ji, Qifu Qu, Junhua Gu, Meng Wang, Yiwei Zhao

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引用次数: 0

摘要

本文利用T-S模糊模型研究了非线性神经网络系统的同步问题。在一个凸优化框架下，得到了一个改进的渐近稳定性条件，以保证模糊驱动神经网络与响应神经网络的同步。通过引入几个辅助矩阵乘法器，增加了自由度，进一步降低了保守性。仿真研究表明了该方法的有效性。

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Improved Synthesis and Analysis Results on Synchronization of T-S Fuzzy Neural Network Systems

This paper studies the synchronization problem for nonlinear neural network systems (NNSs) via T-S fuzzy models. Under a convex optimization framework, an improved asymptotic stability condition is obtained to ensure the synchronization of the fuzzy drive NNS with the response NNS. By introducing several auxiliary matrix multipliers, increased freedom are involved and the conservativeness can be further reduced. Simulation studies are given to show the effectiveness of the proposed method.

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2021 11th International Conference on Information Science and Technology (ICIST)

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