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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.3
- 수록면
- 27 - 32 (6page)
- DOI
- 10.5370/KIEEP.2020.69.1.27
이용수
초록· 키워드
오류제보하기
Artificial neural networks technique is significantly applied to solve complicated engineering and scientific problems in the fields of pattern recognition, robot controls, autonomous driving systems, computer games, etc. This paper presents novel neural networks models employed multiple bias input channels particularly for signal processing applications. We design three types of neural networks model including single-layer and multilayer perceptron, and recurrent networks in which multiple bias signals in the first layer are transfered to the next layer. Additionally, an analytical study is carried out to seek local stability, controllability, and observability against the proposed recurrent network model. Lastly, computer simulation is numerically conducted to demonstrate reliability of the proposed 3-layer perceptron model that works to solve mapping problems
목차
Abstract
1. 서론
2. 다중 바이어스 채널을 갖는 단층 퍼셉트론 모델
3. 다중 바이어스 채널을 갖는 다층 퍼셉트론
4. 재귀형 신경회로망 모델
5. 컴퓨터 시뮬레이션
6. 결론
References
참고문헌 (9)
참고문헌 신청
Akira Hirose and Shotaro Yoshida, “Generalization characteristics of complex-valued feedforward neural networks in relation to Signal coherence,” IEEE Trans. on Neural Networks & Learning Systems, vol. 23, no. 4, pp. 541-551, 2012.
Pascual N. M. Dorantes and Gerardo M. Mendez, “Non-iterative radial basis function neural networks to quality control via image processing,” IEEE Latin America Transactions, vol. 13, no. 10, pp. 3447-3451, 2015.
Cunha Angela, Watanabe Carolina, and Carvalho Carlos, “Medical image segmentation using the Kohonen neural network,” IEEE Latin America Transactions, vol. 17, no. 2, pp. 297-304, 2019.
Huaguang Zhang, Zhanshan Wang, and Derong Liu, “A comprehensive review of stability analysis of continuous-time recurrent neural networks,” IEEE Trans. on Neural Networks & Learning Systems, vol. 25, no. 7, pp. 1229-1262, 2014.
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille, “DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,” IEEE Trans. on Pattern Analysis & Machine Intelligence, vol. 40, no. 4, pp. 834-848, 2018.
Pascual N. M. Dorantes and Gerardo M. Mendez, “Non-iterative radial basis function neural networks to quality control via image processing,” IEEE Latin America Transactions, vol. 13, no. 10, pp. 3447-3451, 2015.
Cunha Angela, Watanabe Carolina, and Carvalho Carlos, “Medical image segmentation using the Kohonen neural network,” IEEE Latin America Transactions, vol. 17, no. 2, pp. 297-304, 2019.
Huaguang Zhang, Zhanshan Wang, and Derong Liu, “A comprehensive review of stability analysis of continuous-time recurrent neural networks,” IEEE Trans. on Neural Networks & Learning Systems, vol. 25, no. 7, pp. 1229-1262, 2014.
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille, “DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,” IEEE Trans. on Pattern Analysis & Machine Intelligence, vol. 40, no. 4, pp. 834-848, 2018.
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UCI(KEPA) : I410-ECN-0101-2020-560-000433202