Title :
Optimisation of digital learning networks when applied to pattern recognition of mass spectra
Author :
Stonham, T.J. ; Aleksander, I.
Author_Institution :
University of Kent at Canterbury, Electronics Laboratories, Canterbury, UK
Abstract :
A pattern classifier employing n-tuple sampling digital learning networks is analysed to show that redundancy can occur both due to the common occurrence of sets of n-tuples of the sample pattern and invariant points in the patterns. Some experimental results are given for a mass-spectrum classifier, where the system has been optimised by reconnection to reduce this redundancy.
Keywords :
learning systems; logic design; optimisation; pattern recognition; redundancy; digital learning networks; learning systems; logic design; mass spectra; optimisation; pattern recognition; redundancy;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19740239