Title :
A neuro-fuzzy system for prediction of pulp digester K-number
Author :
Musavi, M.T. ; Domnisoru, C. ; Smith, G. ; Coughlin, D.R. ; Gould, A.L.
Author_Institution :
Dept. of Electr. & Comput. Eng., Maine Univ., Orono, ME, USA
Abstract :
A neuro-fuzzy system (NFS) has been developed for the prediction of K-number (K ) in a continuous wood pulp digester. The NFS architecture uses a K-means fuzzifier and a modified center-average defuzzifier for fuzzy/crisp conversion. The fuzzy rule base is determined from observed input/output data via an iterative rule-confidence matrix training algorithm, and a max-min fuzzy inference engine is used for rule interpretation. A hybrid backpropagation/genetic-algorithm training routine was developed for tuning of all membership functions. K modeling experiments were conducted on six months of observed industrial digester data at a fifteen-minute sample rate. A variable transform was developed and successfully applied to process variable representation, effectively describing the history of an input variable over a specified window of time
Keywords :
backpropagation; fuzzy neural nets; fuzzy set theory; identification; inference mechanisms; iterative methods; knowledge based systems; paper industry; process control; backpropagation; defuzzifier; fuzzy inference engine; fuzzy neural networks; fuzzy rule base; genetic-algorithm; identification; iterative method; k-means fuzzifier; learning routine; membership function tuning; paper industry; wood pulp digester; Artificial neural networks; Chemical processes; Computer architecture; Engines; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Industrial training; Semiconductor device measurement; System identification;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830849
