Hybrid neuro-fuzzy network-priori knowledge model in temperature control of a gas water heater system

Power Quality Improvement Using Cuckoo Search Based Multi-Level Facts Controller

“…The expression for identification of best current parameter is shown as, The architecture for fuzzy controller is shown in figure 6. In order to get the better compensation as compared with PI controller a Mamdani based fuzzy controller s proposed in this paper [9][10]. The given fuzzy inference system is a two input model, generally, it is taken one of the input as error between Vdc and Vdcref and the second input is rate of change of error.…”

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

“…The integration of Fuzzy models along with first-principles knowledge can, as before, be accomplished in parallel (Abonyi et al, 1999;Fu & Barford, 1995b) or in series (van Lith et al, 2002(van Lith et al, , 2003Vieira, Dias, & Mota, 2005). Moreover they can be complementarily combined into an existing hybrid approach, e.g.…”

“…These methods can account for nonlinearities and are relatively computationally inexpensive. In case that the process model equations are not invertible, FLC (Bazaei & Majd, 2003;Hussain, Ho, & Allwright, 2001;Madar, Abonyi, & Szeifert, 2005), sliding mode control (Hussain & Ho, 2004), Model Predictive Con-trol (MPC) (Abonyi et al, 1999;Cubillos, Callejas, Lima, & Vega, 2001;Hermanto et al, 2011;Ibrehem, Hussain, & Ghasem, 2011;Klimasauskas, 1998;Tsen et al, 1996;van Can et al, 1996;Vega et al, 2000;Vega, Lima, & Pinto, 1997), predictive or optimal control (Anderson et al, 2000;Costa et al, 1998;Costa, Henriques, Alves, Maciel Filho, & Lima, 1999;Cubillos & Lima, 1997Schenker & Agarwal, 2000;Vieira et al, 2005) schema can be employed, where FLC and sliding mode control are computational less expensive while MPC or optimal control may provide better performance. When comparing the performances of control schema that utilize hybrid semiparametric models to those using either traditional control methods (such as a self-tuning PID (Xiong & Jutan, 2002), a generalized minimum variance controller (Xiong & Jutan, 2002), a FLC based on a linear model (Hussain et al, 2001) or a MPC based on a linearized model (Anderson et al, 2000)) or to non-parametric model based controllers (Cubillos et al, 2001;Hussain et al, 2001;Ibrehem et al, 2011;Schenker & Agarwal, 2000), it was mostly observed that the hybrid semiparametric model based control schema performed significantly better.…”

Adaptable PID Versus Smith Predictive Control Applied to an Electric Water Heater System

“…In this previews work there were explored this three different modelling types: neuro-fuzzy (Vieira & Mota, 2003), Hammerstein (Vieira & Mota, 2004) and hybrid (Vieira & Mota, 2005) and (Vieira & Mota, 2004a) models that reflex the evolution of the knowledge about the first principles of the system. These kinds of models were used because the system had a nonlinear actuator, time varying linear parameters and varying dead time systems.…”