Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied. Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

中文翻译：

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基于多模型方法的多速率采样非线性系统模糊建模

基于多模型原理，研究了多速率采样数据非线性系统的模糊辨识。首先，具有多速率采样数据的非线性系统可以表示为多个局部工作点的一些线性模型的非线性加权组合。在此基础上，建立了多速率采样非线性系统的模糊模型。利用模糊竞争学习确定模糊模型的前提结构，利用随机梯度下降算法估计模糊模型的结论参数。所提出的识别算法的收敛性是通过使用鞅定理和引理给出的。建立了用于头发质量检测的酸碱滴定PH中和过程的模糊模型，以证明该方法的有效性。