A modular neural network approach to fault diagnosis
IEEE Transactions on Neural Networks
Hybrid intelligent systems applied to the pursuit-evasion game
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Time series analysis of grey forecasting based on wavelet transform and its prediction applications
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Expert Systems with Applications: An International Journal
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A back-propagation neural network is a large-scale dynamical system, most widely-used for scientific prediction. Its application was inhibited largely by the slow convergence rate and over-prolonged training time, primarily the results of inappropriate sample preprocessing for a large initial sample domain. To solve them, this paper introduced fuzzy clustering to scale-down the learning sample set, with representativeness of the whole sample domain. The established network was tested by analyzing the correlation coefficients between measured and predicted results in the least-square one-dimensional linear regression. In the application case, 50 subsamples were clustered out of the 250-sample domain of the S195 diesel engines to train the network of topological structure 8:9:2. The convergence rate was improved approximately 6.3 times. After validating the model by another untrained 10 samples, the correlation coefficients of the working power and the diesel consumption rate were respectively 0.968 and 0.986, indicating that the optimized network was applicable for data mining from a large knowledge pool.