Water supply system is a complicatedly dynamic nonlinear system, it is difficult to simulate by using one or several explicit functions in pipe networks.
由于供水系统是非常复杂的动态非线性系统，在实际管网的运行中，很难以一个或几个统一的显式函数关系描述管网的工况。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
A diagnosis method on abrupt faults of nonlinear system in learning approach is provided by a detector which is designed to detect, identify and diagnose the faults in the dynamic nonlinear system.
针对一类非线性系统给出一种基于学习方法的突变故障诊断方法，设计了一个观测器，以此来检测、辨识和诊断一类非线性系统动态系统的故障。
A diagnosis method on abrupt faults of nonlinear system in learning approach is provided by a detector which is designed to detect, identify and diagnose the faults in the dynamic nonlinear system.
针对一类非线性系统给出一种基于学习方法的突变故障诊断方法，设计了一个观测器，以此来检测、辨识和诊断一类非线性系统动态系统的故障。
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