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中国邮电高校学报：英文版

Recognition of Continuous Digits by Quantum Neural Networks

Lifei

Published Jan 1, 2003• 0 citations
OpenAlex

Abstract

This paper describes a new kind of neural network-Quantum Neural Network (QNN) and its application to recognition of continuous digits. QNN combines the advantages of neural modeling and fuzzy theoretic principles. Experiment results show that more than 15 percent error reduction is achieved on a speaker-independent continuous digits recognition task compared with BP networks.

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This paper describes a new kind of neural network-Quantum Neural Network (QNN) and its application to recognition of continuous digits. QNN combines the advantages of neural modeling and fuzzy theoretic principles. Experiment results show that more than 15 percent error reduction is achieved on a speaker-independent continuous digits recognition task compared with BP networks.
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The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.