Application and Optimization of Image Fuzzy Control Algorithm based on Gaussian Blur in TensorFlow Training
Yongjun Zhang, Feiyang Ma
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.137How to use a DOI?
- TensorFlow, Gaussian fuzzy algorithm, the convolutional neural network, the fuzzy control algorithm.
- When training a convolutional neural network that can interpret picture ambiguity, a large number of pictures with different ambiguities are needed as a training set. This paper introduces a method to adjust the image ambiguity by adjusting the parameters in the Gaussian fuzzy algorithm. Finally, the convolutional neural network training based on TensorFlow migration learning is completed, and the ambiguity judgment of the image is realized, and the optimization effect of the fuzzy control algorithm is verified.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yongjun Zhang AU - Feiyang Ma PY - 2019/04 DA - 2019/04 TI - Application and Optimization of Image Fuzzy Control Algorithm based on Gaussian Blur in TensorFlow Training PB - Atlantis Press SP - 854 EP - 860 SN - 2352-538X UR - https://doi.org/10.2991/icmeit-19.2019.137 DO - https://doi.org/10.2991/icmeit-19.2019.137 ID - Zhang2019/04 ER -