Abstract
Computational intelligence combines fuzzy systems, neural network and evolutionary computing. In this paper, architecture of a neuro–fuzzy integrated system is presented. A new kind of error backpropagation algorithm to adjust the membership functions of each variable and optimise fuzzy rules is developed. To minimise the output error, a variational method for determining globally optimal learning parameters and learning rules for online gradient descent training of multilayer neural network has been proposed. In order to show the effectiveness of the proposed system, simulation for different variety of domain has been performed. The controller for inverted pendulum has been demonstrated. The controller uses error backpropagation algorithm to adjust the membership functions of each variable, optimise fuzzy rules, and identify the inverted pendulum. Neuro–fuzzy integrated system for coronary heart disease has also been simulated. The results suggest that this kind of hybrid system is also suitable for the identification of patients with high/low cardiac risk.
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: International Journal of Intelligent Systems Technologies and Applications
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.
