Combining Boolean Neural Architectures for Image Recognition

This paper presents a completely integrated Boolean neural architecture, where a selforganizing Boolean neural network (SOFT) is used as a front-end processor to a feedforward Boolean network based on goal-seeking principles (GSN f). This paper will evaluate the advantages of the integrated SOFT-GSN...

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Bibliographic Details
Published inConnection science Vol. 9; no. 4; pp. 405 - 418
Main Authors De Carvalho, A., Fairhurst, M. C, Bisset, D. L
Format Journal Article
LanguageEnglish
Published London Taylor & Francis Group 01.12.1997
Taylor and Francis
Taylor & Francis Ltd
Subjects
Applied sciences
Artificial intelligence
Character Recognition
Computer architecture
Computer science
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Logic
Modular Architectures
Neural networks
Pattern recognition. Digital image processing. Computational geometry
Weightless Neural Networks
Image recognition
Self organization
Feedforward neural nets
Optical character recognition
Binary neuron
Neural network
Neural net architecture
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Summary:This paper presents a completely integrated Boolean neural architecture, where a selforganizing Boolean neural network (SOFT) is used as a front-end processor to a feedforward Boolean network based on goal-seeking principles (GSN f). This paper will evaluate the advantages of the integrated SOFT-GSN f over GSN f by showing its increased effectiveness in an optical character recognition task.
ISSN:0954-0091
1360-0494
DOI:10.1080/095400997116612