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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.20 No.4
- 발행연도
- 2020.12
- 수록면
- 336 - 345 (10page)
- DOI
- 10.5391/IJFIS.2020.20.4.336
이용수
초록· 키워드
오류제보하기
In the last two decades, neural networks and fuzzy logic have been successfully implemented in intelligent systems. The fuzzy neural network (FNN) system framework infers the union of fuzzy logic and neural network system framework thoughts, which consolidates their advantages. The FNN system is applied in several scientific and engineering areas. Wherever there is uncertainty associated with the data, fuzzy logic places a vital rule. The fuzzy set can effectively represent and handle uncertain information. The main objective of the FNN system is to achieve a high level of accuracy by including the fuzzy logic in either the neural network structures, activation functions, or learning algorithms. In computer vision and intelligent systems, convolutional neural networks (CNNs) have more popular architectures, and their performance is excellent in many applications. In this paper, fuzzy-based CNN image classification methods are analyzed, and an interval type-2 fuzzy-based CNN is proposed. The experimental results indicated that the performance of the proposed method was good.
목차
Abstract
1. Introduction
2. Literature Survey
3. Comparison of FCNN Architectures
4. Interval Type-2 Fuzzy CNN
5. Experimental Result
6. Conclusions
References
참고문헌 (42)
참고문헌 신청
A. G. Howard / 2014 / Some improvements on deep convolutional neural network based image classification / Proceedings of the 2nd International Conference on Learning Representations
N. Jmour / 2018 / Convolutional neural networks for image classification / Proceedings of 2018 International Conference on Advanced Systems and Electric Technologies (IC ASET) :397~402
M. J. Hsu / 2020 / A convolutional fuzzy neural network architecture for object classification with small training database / International Journal of Fuzzy Systems 22(1):1~10
X. Y. Wu / 2020 / A hand gesture recognition algorithm based on DC-CNN / Multimedia Tools and Applications 79:9193~9205
L. Li / 2020 / One-shot learning gesture recognition based on joint training of 3D ResNet and memory module / Multimedia Tools and Applications 79(9):6727~6757
N. Jmour / 2018 / Convolutional neural networks for image classification / Proceedings of 2018 International Conference on Advanced Systems and Electric Technologies (IC ASET) :397~402
M. J. Hsu / 2020 / A convolutional fuzzy neural network architecture for object classification with small training database / International Journal of Fuzzy Systems 22(1):1~10
X. Y. Wu / 2020 / A hand gesture recognition algorithm based on DC-CNN / Multimedia Tools and Applications 79:9193~9205
L. Li / 2020 / One-shot learning gesture recognition based on joint training of 3D ResNet and memory module / Multimedia Tools and Applications 79(9):6727~6757
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