Fault diagnosis model and learning rule of RBF ANN is studied. Fault diagnosis principle and step of RBF ANN based on rough sets theory is given.

研究了RBF神经网络故障诊断模型及学习规则，给出了基于粗糙集理论的RBF神经网络故障诊断原理和步骤。

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A robust RBF ANN-observer-based fault detection scheme is proposed for a class of multiple state (time-delay) systems with nonlinear perturbations.

基于状态观测器的方法研究了一类具有非线性扰动的多重状态时滞系统的鲁棒故障检测问题。

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We tried template matching and RBF ANN as classify method.

分类器分别尝试了模板匹配方法和RBF神经网络的方法。

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