The learning of multi-output binary neural networks for handwritten digit recognition

A new learning method of multi-output binary neural networks (BNN) is proposed for handwritten digit recognition based on our simulated light sensitive model. The new teaming algorithm guarantees convergence for any binary-to-binary mapping including these multi-output cases, and learns much faster...

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Bibliographic Details
Main Authors: Kim, J.H., Byungwoon Ham, Chen, J.K., Sung-Kwon Park
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Computer networks
Convergence
Feature extraction
Handwriting recognition
Hardware
Neural networks
Neurons
Power line communications
Very large scale integration
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Summary:A new learning method of multi-output binary neural networks (BNN) is proposed for handwritten digit recognition based on our simulated light sensitive model. The new teaming algorithm guarantees convergence for any binary-to-binary mapping including these multi-output cases, and learns much faster than the backpropagation learning algorithm. Neurons in the BNN employ a hard-limiter activation function and integer weights, thus greatly facilitating hardware implementation of BNN using current digital VLSI technology.
DOI:10.1109/IJCNN.1993.713988