中文摘要：

最近，为小摆动差错的快速的察觉的一条途径为连续时间的系统基于确定的学习理论被建议。在这份报纸，一个差错察觉计划经由确定的学习为非线性的分离时间的系统的一个班被建议。由使用确定的学习算法的分离时间的扩展，一般差错工作(即，内部动力学) 内在的正常和非线性的分离时间的系统的差错模式被分离时间的动态光线的基础功能(RBF ) 局部地精确地接近网络。然后，有嵌入的系统动力学的获得的知识的评估者的一个银行被构造，并且一套剩余被获得并且过去常测量监视系统的动力学和训练系统的动力学之间的差别。一个差错察觉决定计划根据最小的剩余原则被介绍，即，一个差错的出现能被比较剩余的大小在一个分离时间的背景检测。差错 detectability 分析被执行，察觉时间的上面的界限被导出。一个模拟例子被给说明建议计划的有效性。

英文摘要：

Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions （i.e., the internal dynamics） underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function （RBF） networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.