Abstract：This paper studies the handwritten digital recognition by the discrete Hopfield neural networks. In the experiment, the noisy handwritten digital image is transferred into the binary signal by the serial-scan mode. The binary modulated signal is transmitted through the neural network with the designed weight matrix and the output storage mode of the network is mapped into digital image. The error rate of the digital image is negatively correlated with the amplitude of the modulated signal, the time interval and the number of coupled neurons in the network. However, as the noise intensity increases, the error rate manifests the aperiodic stochastic resonance effect, and achieves the minimum at the non-zero optimal noise intensity. Under this circumstance, the recovered digital image appears more clearly. These results provide a theoretical basis for further research on the adaptive weight matrix of Hopfield neural network for obtaining the minimum error rate, and also are of significance for the positive role of the randomness in the associative memory of the neural networks.
潘园园, 张力, 段玲玲, 段法兵. 离散Hopfield神经网络的手写数字识别研究[J]. 复杂系统与复杂性科学, 2018, 15(1): 75-79.
PAN Yuanyuan, ZHANG Li, DUAN Lingling, DUAN Fabing. Study of Handwritten Digital Recognition in Discrete Hopfield Neural Networks. Complex Systems and Complexity Science, 2018, 15(1): 75-79.
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