Year-end Sale % OFF 
Abstract
The ART1 neural network algorithm has been implemented in the past for the classifying and grouping of similar vectors from a machine-part matrix. Recently, a new ART1 paradigm which involves reordering of the input vectors with a modified procedure for storing a group's representation vectors has proven successful in both speed and functionally compared to previous techniques. This new paradigm is now adapted and implemented on a neuro-computer utilising 256 processors, allowing the neural network to take advantage of its inherent parallelism. Tremendous improvements in the speed of the machine-part matrix optimization result from the parallel implementation. Comparisons with the previous serial algorithm are made and suggestions for possible parallel implementation within a manufacturing environment are discussed.
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.
