Guide to pattern recognition using random-access memories
作者： I. Aleksander , T.J. Stonham
关键词:
摘要: About 12 years of work with a specific type learning pattern-recognition system are reviewed. The principles and characteristics the scheme, which is based on random-access-memory implementation, discussed in some detail. Methods improving performance cost-optimising pattern recognisers presented, together case studies variety fields including recognition alphanumerics, chemical data faults digital circuit boards.
sci-hub.se 参考文章(5)
I. Aleksander, Z. Al-Bandar, Adaptively designed test logic for digital circuits Electronics Letters. ,vol. 13, pp. 466- 467 ,(1977) , 10.1049/EL:19770336
T.J. Stonham, I. Aleksander, M. Camp, M.A. Shaw, W.T. Pike, Automatic classification of mass spectra by means of digital learning nets Electronics Letters. ,vol. 9, pp. 391- 394 ,(1973) , 10.1049/EL:19730290
I. Aleksander, Microcircuit learning nets: Hamming-distance behaviour Electronics Letters. ,vol. 6, pp. 134- 136 ,(1970) , 10.1049/EL:19700092
T.J. Stonham, Improved Hamming-distance analysis for digital learning networks Electronics Letters. ,vol. 13, pp. 155- 156 ,(1977) , 10.1049/EL:19770110
T.J. Stonham, I. Aleksander, Optimisation of digital learning networks when applied to pattern recognition of mass spectra Electronics Letters. ,vol. 10, pp. 301- 302 ,(1974) , 10.1049/EL:19740239