Abstract: An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification.

Abstract: A wireless channel monitoring and simulation device with multi-input multi-output (MIMO) is provided, which includes: a wireless channel monitor, configured to collect characteristic parameters of wireless channels in typical environments, and establish models based on the characteristic parameters; a model database, configured to store the characteristic parameter models and parameterize; an original signal, configured to input N different original signals; a wireless channel simulator, configured to simulate a typical channel environment according to the model database configuration, so that the original signal is the same as that in a real typical channel environment, and adopts a N-path output; an N-channel oscilloscope, configured to observe specific waveforms of N-path simulated signals; and a master computer software, configured to process, analyze, and store N-path output signals.

Abstract: A wireless channel monitoring and simulation device with multi-input multi-output (MIMO) is provided, which includes: a wireless channel monitor, configured to collect characteristic parameters of wireless channels in typical environments, and establish models based on the characteristic parameters; a model database, configured to store the characteristic parameter models and parameterize; an original signal, configured to input N different original signals; a wireless channel simulator, configured to simulate a typical channel environment according to the model database configuration, so that the original signal is the same as that in a real typical channel environment, and adopts a N-path output; an N-channel oscilloscope, configured to observe specific waveforms of N-path simulated signals; and a master computer software, configured to process, analyze, and store N-path output signals.

Abstract: An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification.