Abstract—In this paper, an application of dynamic
neuro-fuzzy systems is presented for modeling the subsystems
of the heat recovery steam generator (HRSG). The dynamic
neuro-fuzzy models were developed based on the formal NARX
models topology. The clustering techniques were employed to
define the structure of the fuzzy models by dividing the entire
operating regions into smaller subspaces. The optimal cluster
centers and corresponding membership functions are captured
by FCM, where the parameters of consequent were adjusted by
recursive LSE method. A comparison between the responses of
the proposed models and the responses of the plants ware
preformed, which validates the accuracy and performance of
the modeling approach.
Index Terms—Power plant; HRSG boiler; fuzzy system; experimental data; clustering technique.
A. Ghaffari is with the Department of Mechanical Engineering at the K.N. Toosi University of Technology, Tehran, Iran (e-mail: firstname.lastname@example.org). A. Chaibakhsh is with the Department of Mechanical Engineering at the University of Guilan, Rasht, Guilan, Iran (e-mail: email@example.com). S. Shahhoseini is M.Sc. student at South Tehran Brach, Islamic Azad University, Tehran, Iran (e-mail: firstname.lastname@example.org).
Cite:A. Ghaffari, A. Chaibakhsh, and S. Shahhoseini, "Neuro-Fuzzy Modeling of Heat Recovery Steam Generator," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 604-608, 2012.