Abstract
Conventional fuzzy system design involves finding optimal numbers, shapes and fine tuning of membership functions in the input and output universes of discourse. One way to circumvent a trial-and-error approach is to use expert knowledge. When expert knowledge is not available or is insufficient, this drawback can be overcome by combining the advantages of neural networks which have well established supervised and unsupervised learning algorithms available, and fuzzy systems. In this paper an approximate simplified fuzzy reasoning method is introduced. This method can automatically determine the optimal number and locations of pseudo antecedent and consequent rules on input and output universes of discourse for a given data driven fuzzy system. A self-learning algorithm for this proposed neuro-fuzzy architecture is developed and its applications to several dynamic system identification problems are presented.
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 callDisclaimer: 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.
