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科研成果第17期 ②

24

2016-11

第17期 ②

来源:系统管理员    浏览次数:2535

报 告 人:Sergey Mosin 教授
                  俄罗斯喀山联邦大学
主 持 人:黄哲学
日      期:2016 年 11月 2 日
时      间:下午 2:30-4:30
地      点:计软学院623会议室

Part I: An Approach to Construction the Neuromorphic Classifier for Analog Fault Testing and Diagnosis

The alternative approach to fault dictionary (FD) construction consists in implementation the neuromorphic classifier based one artificial neural networks (ANN). Fault dictionary based on ANN allows eliminating many problems of the parametric FD. An ANN with fixed structure can be used for representing the different quantity of considered fault-free and faulty an analog circuit conditions, thus only the limited small number of coefficients describing synaptic links should be stored. The ANN provides essential time reduction on the responses comparison due to associative operation, i.e. the indicator of T&D passing is generated immediately at the output layer after application of the CUT's output response to neurons in input layer.
Main problems of neuromorphic classifier construction are the complexity of selection the structure of ANN, representation of the CUT's output responses and training of a neural network. Due to the component tolerances and nonlinearities, the output responses of fault-free and faulty analog circuits can be similar and provide wrong test and diagnosis result. Selecting the essential characteristics of output responses, which provide a distinguishability of fault-free and faulty behavior of the CUT is important in order to ensure the convergence during ANN training. The number of such characteristics for each response defines the number of neurons in the input layer of ANN. The data quantity of training set increases greatly with increasing the number of fault conditions, number of training patterns and quantity of used characteristics at each pattern. The redundancy of the essential characteristics set can lead to the overfitting of ANN and loss the accuracy at T&D.
The approach to construction the neuromorphic classifier improving the efficiency of fault T&D and solving the problems of training the appropriate ANN is presented in the paper. The approach takes into account the component tolerances and is based on the use of wavelet coefficients for representation the CUT's output responses, faults clustering and selection only such wavelet coefficients, which provide maximum distances between all pairs of considered circuit’s conditions (fault-free and all faulty).

 

Part II: Quality Improvement of Analog Circuits Fault Diagnosis Based on ANN Using Clusterization as Preprocessing

The infinite set of possible responses corresponds to a fault-free and each of faulty conditions due to component tolerances. Moreover the tolerances have casual nature and can lead to generation the subset of similar output responses for different faults. Consideration of the first circumstance at a fault dictionary construction is provided by representing each controlled parameter in the form of a range of acceptable values. The second circumstance can cause the detection error or degrading the resolving ability of a diagnostic test. The set of output responses with sufficient cardinality corresponding to each circuit condition is used in the case of ANN training. The values of output neurons are become sensitive to values of corresponding applied responses in results of ANN training. The overlapping of training patterns used for different circuit conditions can cause the problems of convergence or overfitting during an ANN training due to the second circumstance.
The technique of quality improvement of the analog circuits faults diagnosis using artificial neural network owing to: 1) preliminary clustering the output responses of considered faults before the ANN training, and 2) probabilistic prediction of presence the detected fault in the frame of each cluster during testing is proposed. The clusterization ensures localization the overlapping subsets of output responses for different faults, allowing to reduce the uncertainty of used training patterns.

 




 

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