The paper deals with fault isolation in nonlinear analogue circuits with tolerance under an insufficient number of independent voltage measurements. The L1-norm optimisation problem for different scenarios of nonlinear fault diagnosis is formulated with a new fast method being presented. How to solve the L1-norm optimisation problem is discussed and a new neural network-based approach for solving the nonlinear constrained L1-norm optimisation problem is proposed and utilised in locating the most likely faulty elements in nonlinear circuits. The validity of the proposed method is verified and simulation examples are presented.

A fault identification approach for nonlinear analogue systems is presented. A fuzzy neural network is developed based on the improving fuzzy weighted reasoning method. The training of network weights and optimization of membership functions are conducted employing genetic algorithms. Fuzzy rules can be realized through the refresh of the weights of the neural network. The availability of the method is examined by simulated test examples.

提出了两种通用有源电流模式滤波器：1.用两个电流传送器（CCII）实现的电流模式三输入单输出二阶滤波器新电路,选择不同的输入信号组合即能获得低通、高通、带通、陷波和全通五种二阶滤波函数；2.由CCII和多输出端跨导运算放大器（OTA）实现的单输入多输出滤波器新电路。所提电路结构简单、中心频率ω0和品质因数Q独立可调、无源元件全部接地，滤波器特性参数对无源元件灵敏度低。理论分析和计算机仿真表明所提电路方案正确。

本文提出了一种新的利用跨导运放OTA精确实现电压-电流型硬限幅函数神经元方法，它的主要特点是容易实现、且神经元的输出幅值可调。本文所提的基于OTA的硬限幅函数神经元（简称OHL）非常适合于求解L1问题的神经网络跨导模式综合实现。实例和计算机仿真结果与理论分析相吻合。

According to the principle of fault detect and diagnosis, this paper presents a new technology of data mining to deal. with a lot of data got from fault detection and diagnosis. A method using database technology to solve fault detection and diagnosis is developed.