Adaptive neuro-fuzzy inference systems are used to develop the inferential sensor model for estimating the average air temperature in space water heating systems. Fuzzy inference system structure identification and parameter selection for structure training are the key factors for system performance. This paper describes grid partition based fuzzy inference system, named ANFIS-GRID. The impact of selection of proper parameters for training process using ANFIS-GRID is presented. Results demonstrate that selection of number of MFs, step size and step size increase rate affect the performance of the model. Subtractive clustering has been applied for FIS generation for average air temperature estimation by S. Jassar et al. [5]. This paper presents the grid-partition based generation of FIS structure, ANFIS-GRID. The model performance is affected by the training process through which the relevant parameters are determined. The paper presents the analysis and selection of proper parameters for training of ANFIS-GRID structure. The best training and testing data sets for better performance of model are presented. Fig. 1 Block diagram representation of closed loop boiler control scheme [5]
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