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has been cited by the following article:

• TITLE: An Optimization of Neural Network Hyper-Parameter to Increase Its Performance

  AUTHORS: Yinxuan Fu

  KEYWORDS: Learning Efficiency, Neural Network, Intermediate Values

  JOURNAL NAME: Intelligent Information Management, Vol.10 No.4, July 11, 2018

  ABSTRACT: With the boost of artificial intelligence, the study of neural network intrigues scientists. Artificial neural network, which was first designed theoretically in 1943 based on understanding of human brains, demonstrated impressing computational and learning capabilities. In this paper, we investigated the neural network’s learning capability by using a feed-forward neural network to recognize human’s digit hand-writing. Controlled experiments were executed by changing the input values of different parameters, such as learning rates and hidden layer units. After investigating upon the effects of each parameter on the overall learning performance of the neural network, we concluded that, when an intermediate value of one given parameter was implemented, the neural network achieved the highest learning efficiency, and potential problems like over-fitting would be prevented.