A systematic method based on a neural network that utilizes a genetic algorithm (GNN) and the deviation space to diagnose faulty behavior in analog circuits under test (CUTs) is presented in the paper. To reduce the computational requirement of network simulations, we derive a unified fault feature, which can be extracted from measurable voltage deviation in the deviation space. The extracted unified feature vectors for single, double, and triple faults are characterized on the basis of measurable voltage deviation in the deviation space. Then, the faults can be classified by applying a neural network (NN) whose inputs are extracted from independent measurements - the transfer impedances at accessible nodes or the corresponding feature of various faults. It is applicable to linear circuits as well as nonlinear ones. The method presented minimizes the online measurements and offline computation. Illustrative examples verify the effectiveness of the proposed method.

A systematic method for fault diagnosis of analogue circuits based on the combination of neural networks and wavelet transforms is presented. Using wavelet decomposition as a tool for removing noise from the sampled signals, optimal feature information is extracted by wavelet noise removal, multi-resolution decomposition, PCA (principal component analysis) and data normalisation. The features are applied to the proposed wavelet neural network and the fault patterns are classified. Diagnosis principles and procedures are described. The reliability of the method and comparison with other methods are shown by two active filter examples.

In this study an adaptive impedance tuning scheme based on the quantum genetic algorithm (QGA) is proposed. A low‐pass passive LC impedance matching network is used and tuned by a QGA controller. The tuning algorithm iterates until the matching point is reached. Simulations and results of the QGA tuning method are presented for 1.8 and 2.4 GHz of mobile and wireless communications with different antenna impedances. Comparison of the proposed QGA tuning method with conventional genetic algorithm‐based tuning method is also given, which shows that the QGA tuning algorithm is much faster. The proposed QGA method can also be used for tuning a radiofrequency band by tuning the impedance matching network to the band centre frequency. Simulation results for the universal mobile telecommunications system band of 60 MHz width and Industrial, Scientific and Medical band of 200 MHz width all show good performances.

In cyber–physical power systems (CPPSs), the interaction mechanisms between physical systems and cyber systems are becoming more and more complicated. Their deep integration has brought new unstable factors to the system. Faults or attacks may cause a chain reaction, such as control failure, state deterioration, or even outage, which seriously threatens the safe and stable operation of power grids. In this paper, given the interaction mechanisms, we propose an interdependent model of CPPS, based on a characteristic association method. Utilizing this model, we can study the fault propagation mechanisms when faulty or under cyber-attack. Simulation results quantitatively reveal the propagation process of fault risks and the impacts on the CPPS due to the change of state quantity of the system model.