Abstract
While building stochastic configuration networks (SCNs), there is no guarantee that the randomly generated weights will satisfy the supervisory mechanism and that the adopted weights will significantly reduce the training error. This paper extends SCN by applying the particle swarm optimisation (PSO) technique for one-step optimising of the set of random weights generated. These optimised weights provide a higher likelihood of satisfying the supervisory mechanism and improving the learning rate. Simulations are carried out over five regression and three classification datasets. Results demonstrate that an improved training rate and generalisation can be achieved.
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 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.
