Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Model-based predictive control for spatially-distributed systems using dimensional reduction models
International Journal of Automation and Computing
Generalized clustering networks and Kohonen's self-organizing scheme
IEEE Transactions on Neural Networks
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According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process, a combustion working condition recognition method based on the generalized learning vector (GLVQ) neural network is proposed. Firstly, the numerical flame image is analyzed to extract texture features, such as energy, entropy and inertia, based on grey-level co-occurrence matrix (GLCM) to provide qualitative information on the changes in the visual appearance of the flame. Then the kernel principal component analysis (KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly. Finally, the GLVQ neural network is trained by using the normalized texture feature data. The test results show that the proposed KPCA-GLVQ classifier has an excellent performance on training speed and correct recognition rate, and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.