Abstract
In complex manufacturing, the system parameters have dynamic and nonlinear characters. Existing parameters setting methods show low efficiency and accuracy, and some setting experience accumulated in engineering practice can not be fully used. Therefore, an online parameter setting method with improved adaptive neuro-based fuzzy inference model is proposed in this paper. The advantages of ANFIS in self-learning and fuzzy inference method of fuzzy mathematics are combined effectively to determine the fuzzy rules and fuzzy membership. Then the neural network predictor is introduced to enhance the adaptive ability and nonlinear approximation ability of fuzzy neural network. The parameters and the structure of fuzzy rules are regulated and updated by the nearest neighbor clustering algorithm, which improves the accuracy of fuzzy inference and enhances the fault-tolerance and robustness of system. In our experiments, by the simulations of transformer fault diagnosis and soldering technology, we find improved ANFIS model can better reflect the actual operating state of transformer and it can make modeling and control for nonlinear I/O system in the manufacturing process like soldering. The improved system is also proved by experiments to have better performance in robustness and convergence.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.