Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
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The aim is to develop simple Sugeno neuro-fuzzy predictive controller to improve the dynamic performance of control systems of nonlinear plants under uncertainties. The controller is designed by ANFIS of MATLAB and is successfully applied for the control of the biogas production rate in the anaerobic digestion of organic waste in waters. The main contributions concern the design of a controller configuration, combining a developed Sugeno PI-type neurofuzzy controller as a simplified equivalent to a two-level Mamdani controller with auto-tuning scaling coefficients and a Sugeno plant predictor and its implementation for the control of wastewater treatment processes. The improvement of the dynamic performance of control system with the Sugeno neuro-fuzzy predictive controller has been proven by simulation in comparison to various other systems.