A double level fusion architecture based intelligence algorithms for lumber drying parameters detection system
- Resource Type
- Conference
- Authors
- Liu, Yuan-Ze; Zhang, Jia-Wei; Li, Ming-Bao
- Source
- 2010 International Conference on Machine Learning and Cybernetics Machine Learning and Cybernetics (ICMLC), 2010 International Conference on. 1:339-344 Jul, 2010
- Subject
- Computing and Processing
Communication, Networking and Broadcast Technologies
Moisture
Temperature measurement
Data models
Resistance
Classification algorithms
Training
Moisture measurement
Multi-modeling
FCM
RBFNN
Lumber moisture content
- Language
- ISSN
- 2160-133X
2160-1348
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.