PATTERN RECOGNITION ABILITIES OF RECURRENT NEURAL NETWORKS

  • Nguyen Quang Hoan Hung Yen University of Technology and Education
  • Vu Thi Them Center for Education and Training-Hanoi Gia Loc
  • Bui Dinh Quan People's Committee of Dong Hung District
Keywords: Recurrent neural networks, Hopfield, BAM neural networks, Liapunov stability, pattern recognition, learning rule

Abstract

The artificial neural networks are simulating the human brain. Could artificial neural networks memorize as the human brain? The paper presents the structures, the learning rules and the stability of the Hopfield, Bidirectional Associative Memory (BAM), two main recurrent neural networks. We also perform the experiments on their memory abilities, ability of fault isolation for several of failure bits. An example of pattern recognition of image faces and corresponding their labels are also represented.

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Published
2017-04-12
How to Cite
Nguyen Quang Hoan, Vu Thi Them, & Bui Dinh Quan. (2017). PATTERN RECOGNITION ABILITIES OF RECURRENT NEURAL NETWORKS . UTEHY Journal of Science and Technology, 13, 44-49. Retrieved from http://tapchi.utehy.edu.vn/index.php/jst/article/view/212