Title :
Hybrid neuro-fuzzy system application to inferential sensing
Author :
Jassar, S. ; Zhao, L. ; Liao, Z.
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
Abstract :
As the neuro-fuzzy system is based on a feed forward network structure, it cannot effectively cope with dynamic processes such as space heating systems for the built environment. To overcome this problem, an improved version of the hybrid system is developed and presented in this paper. This system has self-feedback loops for the output that can model the dynamical behavior of the process. The developed model is used in a case study for the estimation of average air temperature in the buildings served by a forced air space heating systems. The results show the effectiveness of the self-feedback system in terms of minimization of root mean squared error.
Keywords :
feedback; feedforward; fuzzy control; mean square error methods; minimisation; neurocontrollers; space heating; temperature control; average air temperature estimation; feedforward network structure; forced air space heating systems; hybrid neuro-fuzzy system; inferential sensing; root mean squared error minimisation; self-feedback system; Application software; Feeds; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Input variables; Multi-layer neural network; Neural networks; Resistance heating; Space heating; Adaptive Systems; Fuzzy Logic; Inferential Sensing; Neuro-Fuzzy Systems;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on
Conference_Location :
Taichung
Print_ISBN :
978-1-4244-5045-9
Electronic_ISBN :
978-1-4244-5046-6
DOI :
10.1109/ICIEA.2010.5514788
