一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號碼
標(biāo)題
留言內(nèi)容
驗(yàn)證碼

前饋型神經(jīng)網(wǎng)絡(luò)容錯性研究的現(xiàn)狀和展望

楊良土,  胡東成,  羅予頻

楊良土, 胡東成, 羅予頻. 前饋型神經(jīng)網(wǎng)絡(luò)容錯性研究的現(xiàn)狀和展望[J]. 電子與信息學(xué)報, 1998, 20(6): 840-846.
引用本文: 楊良土, 胡東成, 羅予頻. 前饋型神經(jīng)網(wǎng)絡(luò)容錯性研究的現(xiàn)狀和展望[J]. 電子與信息學(xué)報, 1998, 20(6): 840-846.
Yang Liangtu, Hu Dongcheng, Luo Yupin. RESEARCHES ON FAULT TOLERANCE OF FEEDFORWARD NEURAL NETWORKS--STATUS AND PROSPECTS[J]. Journal of Electronics & Information Technology, 1998, 20(6): 840-846.
Citation: Yang Liangtu, Hu Dongcheng, Luo Yupin. RESEARCHES ON FAULT TOLERANCE OF FEEDFORWARD NEURAL NETWORKS--STATUS AND PROSPECTS[J]. Journal of Electronics & Information Technology, 1998, 20(6): 840-846.

前饋型神經(jīng)網(wǎng)絡(luò)容錯性研究的現(xiàn)狀和展望

RESEARCHES ON FAULT TOLERANCE OF FEEDFORWARD NEURAL NETWORKS--STATUS AND PROSPECTS

  • 摘要: 本文首先明確具體地給出了前饋型神經(jīng)網(wǎng)絡(luò)(以下簡稱前饋網(wǎng)絡(luò))容錯性的基本概念及其研究內(nèi)容,進(jìn)而系統(tǒng)地對前饋網(wǎng)絡(luò)容錯性研究的各種分析和設(shè)計方法進(jìn)行了簡要的介紹和評述。最后提出了前饋網(wǎng)絡(luò)容錯性有待進(jìn)一步研究的若干主要問題。
    Abstract: The concept of fault tolerance of feedforward neural networks and its contents for studying are given clearly and specially. Then, systematically and concisely, various methods for analyzing and increasing the fault tolerance of feedforward neural networks are introduced and evaluated. Finally, some main problems for further researches on the fault tolerance of feedforward neural networks are proposed.
    • HTML全文
  • Nijhuis, Jos,et al. Limits to the fault-tolerance of a feed forward neural network with learning.Proceedings of the 20th International Symposium on Fault-Tolerant Computing, Chapel Hill, NC, USA: 1990, 228-235.[2]Zhong Zhang, Ruwei Dai. Fault tolerance measure of neural networks. Proceedings of the 3rd Pacific Rim International Conference on Artificial Intelligence, Beijing, China: 1994, I-506-I-511.[3]楊士元,田志宇,童詩白.前向神經(jīng)網(wǎng)絡(luò)的故障性能.清華大學(xué)學(xué)報,1995,35(4): 88-94.[4]Chalapathy Neti, et al. Maximally fault tolerant neural networks. IEEE Trans. on Neural Networks, 1992, NN-3(1): 14-23.[5]徐海銀,陳幼平,周祖德,何頂新.容錯神經(jīng)網(wǎng)絡(luò)及容錯BP算法研究.華中理工大學(xué)學(xué)報,1995, 23(2): 22-26.[6]Kerlirzin P, Refregier P. Theoretical investigation of the robustness of multilayer perceptrons: Analysis of the linear case and extention to nonlinear networks. IEEE Trans. on Neural Networks, 1995, NN-6(3): 560-571.[7]田志宇.與性能相關(guān)的可靠性評估方法及ANN可靠性的研究:[博士學(xué)位論文].北京:清華大學(xué),1994.[8]Segee B E, Carter M J. Comparative fault tolerance of generalized radial basis function and multilayer perceptron networks. Proceedings of the 1993 IEEE International Conference on Neural Networks, San Francisco, CA, USA: 1993, 1847-1852.[9]Phatak D S, Koren I. Complete and partial fault tolerance of feedforward neural nets. IEEE Trans. on Neural Networks, 1995, NN-6(2): 446-456.[10]Damarla T R, Bhagat P K. Fault Tolerance of Neural Networks. Proceedings of IEEE SOUTHEASTCON-Energy and Information Technology in the Southeast, Columbia, SC, USA: 1989, I-328-I-331.[11]Emmerson M D, et al. Fault tolerance and redundancy of neural nets for the classification of acoustic data. Proceedings of ICASSP 91, Toronto, Ont., Can.: 1991, II-1053-II-1056.[12]Emmerson M D, Damper R I. Determining and improving the fault tolerance of multilayer perceptrons in a pattern-recognition application. IEEE Trans. on Neural Networks, 1993, NN-4(5): 788-793.[13]Pomerleau D A. Reliability estimation for neural network based autonomous driving[J].Robotics and Autonomous Systems.1994, 12(3-4):113-119[14]George B. Fault tolerance and robustness in neural networks. Proceedings of IJCNN, Seattle, WA, USA: 1991, II-986.[15]Chun-shin Lin, Ing-Chyuan Wu. Maximizing fault tolerance in multilayer neural networks. Proceedings of 1994 IEEE International Conference on Neural Networks, Orlando, FL, USA: 1994, I-419-I-424.[16]Sequin C H, Clay R D. Fault tolerance in artificial neural networks. Proceedings of IJCNN, San Diego, CA, USA: 1990, I-703-I-708.[17]Ching-Tai Chiu, et al. Training techniques to obtain fault-tolerant neural networks. Proceedings of the 24th International Symposium on Fault-Tolerant Computing, Austin, TX, USA: 1994, 360-369.[18]Murray A F, Edwards P J. Synaptic weight noise during multilayer perceptron training: Fault tolerance and training improvements. IEEE Trans. on Neural Networks, 1993, NN-4(4): 722-725.[19]Murray A F, Edwards P J. Enhanced M L P. Performance and fault tolerance resulting from synaptic weight noise during training. IEEE Trans. on Neural Networks, 1994, NN-5(5): 792-802.[20]肖本政.前饋網(wǎng)絡(luò)的性能及學(xué)習(xí)算法改進(jìn)的研究:[博士學(xué)位論文].北京:清華大學(xué),1992.[21]Minnix Jay I. Fault tolerance of the backpropagation neural network trained on noisy inputs. Proceedings of IJCNN, Baltimore, MD, USA: 1992, III-847-III-852.[22]Minnix Jay I. An analysis of the effects of noisy training sets on the fault tolerance of neural networks. Proceedings of 1991 IEEE International Conference on Systems, Man, and Cybernetics, Charlottesville, VA, USA: 1991, II-713-II-718.[23]Ching-Tai Chiu, et al. Modifying training algorithms for improved fault tolerance. Proceedings of 1994 IEEE International Conference on Neural Networks, Orlando, FL, USA: 1994, I-333-I-338.[24]Phatak D S, Koren I. Fault tolerance of feedforward neural nets for classification tasks. Proceedings of IJCNN, Baltimore, MD, USA: 1992, II-386-II-391.[25]姚增起.用不可靠元件構(gòu)造可靠系統(tǒng)及其神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn).自動化學(xué)報,1990, 16(5): 429-435.[26]魏徽,疏松桂.神經(jīng)網(wǎng)絡(luò)控制器的可靠性研究與設(shè)計.控制系統(tǒng)可靠性研究--研究成果匯編,北京:中國科學(xué)院自動化研究所,1994: 124-129.[27]Ekong D U, et al. Fault tolerant neural networks for control systems. Proceedings of 1993 IEEE WESCANEX, Saskatoon, Ont., Can:1993, 269-275.[28]Yuang-Ming Hsu, et al. Time-redundant multiple computation for fault-tolerant digital neural networks. Proceedlings of ISCAS 95，Seattle, WA, USA: 1995, II-977-II-980.[29]Lon-Chan Chu, Wah B W. Fault tolerant neural networks with hybrid redundancy. Proceedings of IJCNN, San Diego, CA, USA: 1990,11-639-11-649.[30]Jung H Kim, et al. Fault-tolerant artificial neural networks. Proceedings of IJCNN, Seattle, WA, USA: 1991, 11-951.[31]Chung-Hsing Chen, et al. Reconfigurable fault tolerant neural network. Proceedings of IJCNN, Baltimore, MD, USA: 1992, II-547-II-552.[32]Khunasaraphan C, et al. Weight shifting techniques for self-recovery neural networks. IEEE Trans. on Neural Networks, 1994, NN-5(4): 651-658.[33]Khunasaraphan C, et al. Recovering faulty self-organazing neural networks: By weight shifting technique. Proceedings of 1994 IEEE International Conference on Neural Networks, Orlando, FL, USA: 1994, III-1513-III-1518.
  • 加載中
計量
  • 文章訪問數(shù):  2716
  • HTML全文瀏覽量:  252
  • PDF下載量:  720
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  1997-03-10
  • 修回日期:  1998-05-17
  • 刊出日期:  1998-11-19

目錄

    /

    返回文章
    返回