Soft computing applications in dynamic model identification of polymer extrusion process

This paper proposes the applications of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a `grey-box model' or has been termed as a `semi-physical model' in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy rule-based system (FRBS) is introduced into the analytical model of extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing an optimal structure for each sub-model, a hybrid algorithm of genetic algorithm with fuzzy system (GA-fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions (MFs). The developed model is adaptive with its learning ab...

• Type: Journal
• Year: 2004
• Where: ASC
• Authors: Leong Ping Tan, Ahmad Lotfi, Eugene Lai, J. B. Hull
• Keywords: ASC 2004, ASC 2006, Extrusion Process, Model, Model Prediction