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GRBF neural network and frequency spectrum analysis are applied to fault diagnosis for component of power electronic equipment in certain facility. By collecting the voltage signals of key points and proceeding Fourier analysis, the structural parameters of GRBF network are determined. Fault determination mode is established. Both experimental and simulation results show that the proposed method is...
The credit scoring has been regarded as a critical topic and its related departments make efforts to collect huge amount of data to avoid wrong decision. An effective classificatory model will objectively help managers instead of intuitive experience. This study proposes five approaches combining with the back-propagation neural network (BPN) classifier for features selection that retains sufficient...
On automatic modulation there are two approaches, decision-theoretic and statistical pattern. An automatic modulation recognition system to recognize four digital signal classes as: MASK, MFSK, MPSK, MQAM is proposed in this paper, which using decision-theoretic based feature set addition to statistical pattern based feature set with VLBP(variable learning rate back-propagation)BP neural network and...
Feature reduction is a key step of pattern recognition. In this paper, a new feature reduction method is presented integrated with distance measure and singular value decomposition (SVD). Firstly, feature sensitivity is put forward to distinguish the different classes, namely the different mechanic faults and defined as the ratio of within-class distance to between-class distance. Secondly, sensitivities...
In this study, an enhanced correlation-test-based validation procedure is developed to check the quality of identified neural networks in modeling of nonlinear systems. The new computation algorithm upgrades the validation power by including a direct correlation test between residuals and delayed outputs that have been quoted indirectly in the most previous approaches. Furthermore, based on the new...
Motor systems are highly important and are critical components in industrial processes. Up to 60% of the electricity produced in the U.S. converts into other forms of energy to provide power to equipment through motor [1]. Machinery reliability and performance can be improved with early fault diagnosis and condition monitoring; therefore, the fault diagnosis system for motor has been highlighted for...
In multivariate statistical process control, most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistics. But these charts do not relieve the need for pinpointing the source(s) of the out-of-control signals. Neural networks (NNs) have excellent noise tolerance and high pattern identification capability, which have been used...
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