Abstract
The BP network uses the rule of local decline to slow convergence speed and easy to fall into local optimum. With the increase of BP input parameters lead to the dimension disaster. Taking into consideration the above problems, Using adaptive ant colony optimization algorithm optimized the training process of BP network referred to as AACOABP. The BP network and the BP network optimized by adaptive ant colony algorithm to (AACOABP) were used to identify the faulty type of the collecting samples about DGA fault test data, AACOABP can make the fault identification accuracy is greatly increased, the recognition accuracy from 50% ~ 75% to 87.5% ~ 100%.
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.