Heat transfer dynamics modelling by means of clustering and swarm methods

摘要

This paper deals with the modelling problem of heat transfer dynamics in thermal exchanger process by using fuzzy prediction approaches. Clustering and swarm-based optimisation methods are used to derive heat transfer dynamical models to predict temperature variations of hot and cold fluids in the exchanger. The clustering method relies on a one-shot potential calculating strategy to extract the fuzzy sets distribution from the data space. However, the swarm optimisation method employs a subject function to optimise the premise and conclusion parameters of the fuzzy structure. Experimental data extracted from a pilot exchanger process is used to learn the fuzzy models, and their performances are compared on both training and testing measurement data.