After CNN basic structure was introduced by LeCun in 1989, there has not been a major structure change except for more deep network until recently. The deep network enhances the expression power due to improve the abstraction ability of the network, and can learn complex problems by increasing non linearity. However, the learning of a deep network means that it has vanishing gradient or longer learning time. In this study, we proposes a CNN structure with MLP layer. The proposed CNNs are superior to the general CNN in their classification performance. It is confirmed that classification accuracy is high due to include MLP layer which improves non linearity by experiment. In order to increase the performance without making a deep network, it is confirmed that the performance is improved by increasing the non linearity of the network.

ABSTRACT
 1. 서 론
 2. CNN 구조 및 제안된 CNN 구조
  2.1 CNN
  2.2 제안된 CNN의 구조
 3. 실험 및 결과
 4. 결론 및 고찰
 References

• 박진현(Mechatronics Eng., Gyeongnam Nat‘l Univ. of Science and Technology) | Jin-Hyun Park
• 황광복(Mechatronics Eng., Gyeongnam Nat‘l Univ. of Science and Technology) | Kwang-Bok Hwang
• 최영규(Mechatronics Eng., Gyeongnam Nat‘l Univ. of Science and Technology) | Young-Kiu Choi Corresponding Author