Title :
Parameter selection for training process of neuro-fuzzy systems for average air temperature estimation
Author :
Jassar, Surinder ; Zhao, Lian ; Liao, Zaiyi ; Ng, Ka Long Ringo
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
Abstract :
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.
Keywords :
atmospheric temperature; fuzzy neural nets; inference mechanisms; temperature sensors; ANFIS-GRID; adaptive neuro-fuzzy inference systems; average air temperature estimation; grid partition; inferential sensor model; parameter selection; space water heating systems; structure identification; training process; Adaptive systems; Automatic control; Automation; Biological control systems; Boilers; Fuzzy neural networks; Fuzzy systems; Sensor systems; Temperature control; Temperature sensors; ANFIS; ANFIS training; Average air temperature; Grid partition;
Conference_Titel :
Mechatronics and Automation, 2009. ICMA 2009. International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4244-2692-8
Electronic_ISBN :
978-1-4244-2693-5
DOI :
10.1109/ICMA.2009.5246537
