A double level fusion architecture based intelligence algorithms for lumber drying parameters detection system

To solve the problem that a single model can not precisely describe the global properties of the lumber moisture content (LMC) during the wood drying process, LMC measurement based multi-modeling method is presented in this paper. The method based on double layers intelligent structure which Fuzzy C-Means clustering is classification layer to classify equivalent resistance value, the inlet ambient temperature and the outlet ambient temperature data into subsets which have different cluster centers. The RBFNN and LS-SVM are modeling layers. The deg of membership is used for weighting and meaning the output of each subset to obtain the estimated LMC value as the final output. Experimental simulation results show that multi-modeling method has strong generalization ability and prefer measuring performance.