This paper describes the utilisation of a predictive model for studying the thermal performance of masonry housing based on a neuro-fuzzy approach using a set of training data collected from four test house modules. The room temperatures for the four modules have been predicted from a collection of interior surface temperatures using the ANFIS platform. It has been shown that for the test modules the ANFIS Sugeno-type modelling approach offers an accurate and reliable prediction tool by which given input-output patterns could be achieved with a satisfactory level of accuracy.
14th International Brick & Block Masonry Conference. Proceedings of the 14th International Brick & Block Masonry Conference (Sydney 17-20 February, 2008) p. 567-578