Abstract
Fuzzy logic, neural networks and genetic algorithms are three popular artificial intelligence techniques that are widely used in many applications. Due to their distinct properties and advantages, they are currently being investigated and integrated to form new models or strategies in the areas of system control. In this paper, a neuro-fuzzy controller (referred to as NFCGA) based on the radial basis function neural network is tuned automatically using genetic algorithms (GA). A linear mapping method is used to encode the GA chromosome, which consists of the width and centre of the membership functions, and also the weights of the controller. Dynamic crossover and mutation probabilistic rates are also applied for faster convergence of the GA evolution. Application of the NFCGA to a coupled-tank liquid-level laboratory process is investigated in this paper. Compared to a manually-tuned conventional fuzzy logic controller and a proportional-plus-integral-plus-derivative (PID) controller which are applied to the same process, the NFCGA shows considerable robustness and advantages.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering Applications of Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
