The gas-collector pressure control system of coke oven has the time-varied characteristic with strongly coupli...
Wang Jie-sheng 1, 2 , Gao Xian-wen 1 , Liu Lin 2 1. College of Information Science and Engineering, Northeastern University, Shenyang 110014, China2. School of Electronic and Information Engineering, Liaoning University of Science & Technology, Anshan 114044, China
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 classifer 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.
