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Application of a novel neural network for identification of nonlinear carbon/carbon gaseous-state deposit process

A novel neural network model and algorithm for highly nonlinear C/C gaseous-state deposit process is presented. The network model consists of fuzzy classifier and some wavelet sub-networks called a hybrid neural network. The input samples are trained by a homologous wavelet network after classifying. The results of the identification of the C/C gaseous-state deposit process are desirable.