Earticle

초록

영어

Convolutional neural network (CNN) -- the result of the training is affected by of initial value of the weights. It is concluded that the model is not necessarily the best features of expression. The use of genetic algorithm can help choosing the better characteristics. But there almost was not literature study of the combining genetic algorithm with CNN. So this research has a lot of space and prospects. GACNN convolution genetic neural network model based on random sample has a better solution to obtain the unknown character expression. CNN individual training set uses a random data set. At the same time, the crossover and the mutation genetic algorithm bring random factors. There may are unknown feature expressions that may be appropriate. Experiments are based on accepted MNIST data sets, and the experimental results proved the advantages of the model.

목차

Abstract
 1. Introduction
 2. Algorithm Constitute
  2.1 CNN
  2.2 GA Algorithm
  2.3 The Algorithm after Combining with Two Parts:
 3. Introduction of the Experiment
 4. The Experimental Results
 5. Analysis of Experimental Results
 6. Sum Up the Main Points
 References

키워드

저자

• You Zhining [ School of Computer Technology, Jimei University, Xiamen 361021, China ]
• Pu Yunming [ School of Computer Technology, Jimei University, Xiamen 361021, China ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605