Abstract
The feasibility of the inversion of laser diffraction data for size and shape distribution by neural networks has been investigated by computer simulation. The size and shape density distributions are represented by only four parameters: the peak positions and the full width at half maximum. Compared to the approach whereby the distributions are represented by a histogram with 30 grid points, the results are an order of magnitude less accurate.
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.
