ISSN:
0934-0866
Keywords:
Chemistry
;
Industrial Chemistry and Chemical Engineering
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
,
Process Engineering, Biotechnology, Nutrition Technology
Notes:
An investigation is presented concerning the ability of neural nets to classify particles using contour data. Different nets were trained to classify limestone, quartz and coffee particles by their outer boundaries. The contour lines of the analysed particles were similar and differed only in a complex way. A new method of interpreting the Fourier coefficients is shown, which might lead to a possibility of defining particle shape classes by examples. Information is given concerning the selection and design of the appropriate neural net, e.g. back-propagation, and self-organizing maps. In addition, a possibility of interpreting the trained neural nets is demonstrated.
Additional Material:
5 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/ppsc.19930100511
Permalink