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關(guān)于前饋多層神經(jīng)網(wǎng)絡(luò)多維函數(shù)逼近能力的一個(gè)定理

韋崗 李華 徐秉錚

韋崗, 李華, 徐秉錚. 關(guān)于前饋多層神經(jīng)網(wǎng)絡(luò)多維函數(shù)逼近能力的一個(gè)定理[J]. 電子與信息學(xué)報(bào), 1997, 19(4): 433-438.
引用本文: 韋崗, 李華, 徐秉錚. 關(guān)于前饋多層神經(jīng)網(wǎng)絡(luò)多維函數(shù)逼近能力的一個(gè)定理[J]. 電子與信息學(xué)報(bào), 1997, 19(4): 433-438.
Wei Gang, Li Hua, Xu Bingzheng. A NOVEL THEOREM ON THE MULTI-DIMENSIONAL FUNCTION APPROXIMATION ABILITY OF FEED FORWARD MULTI-LAYER NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 1997, 19(4): 433-438.
Citation: Wei Gang, Li Hua, Xu Bingzheng. A NOVEL THEOREM ON THE MULTI-DIMENSIONAL FUNCTION APPROXIMATION ABILITY OF FEED FORWARD MULTI-LAYER NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 1997, 19(4): 433-438.

關(guān)于前饋多層神經(jīng)網(wǎng)絡(luò)多維函數(shù)逼近能力的一個(gè)定理

A NOVEL THEOREM ON THE MULTI-DIMENSIONAL FUNCTION APPROXIMATION ABILITY OF FEED FORWARD MULTI-LAYER NEURAL NETWORKS

  • 摘要: 本文首次證明了前饋神經(jīng)網(wǎng)絡(luò)多維函數(shù)逼近能力的一個(gè)重要定理:當(dāng)隱層神經(jīng)元數(shù)目足夠多時(shí),其多維函數(shù)逼近能力與維數(shù)無關(guān).也就是說我們只需研究其一維函數(shù)逼近能力,所得的結(jié)論完全適合于多維情形,該定理大大簡化了前饋多層神經(jīng)網(wǎng)絡(luò)函數(shù)逼近問題的分析難度。本文還給出了該定理的一個(gè)應(yīng)用。
  • 韋崗,賀前華.神經(jīng)網(wǎng)絡(luò)模型學(xué)習(xí)及應(yīng)用,北京:電子工業(yè)出版社,1994,第三章.[2]Sam Kwong, Wei Gang, Ouyang Jing-zheng. Discrete utterance recognition based on nonlinear[3]model identification with single layer neural networks. Proc. IEEE Int. Conf. Circuits and Systems, USA:1993, 2419-2422.[4]Wei Gang, Ouyang Jing-zheng. On the bound of the approximation capacity of multi-layer neural[5]networks. Proc. Int. Joint. Conf. Neural Networks, Singapore:1991, 2299-2304.[6]Cybenko G. Approximation勿superposition of a sigmoidal function[J].Math. Control, Signals, Syst.1989, 2(4):303-314[7]Hartman E J, Keeler J D, Kowalski J M. Layered neural networks with Gaussian hidden units as universal approximations[J].Neural Comp.1991, 2(3):210-215[8]Hornik K. Approximation capacities of multilayer feedforward networks[J].Neural Networks.1991, 4(3):251-257[9]Park J, Sandberg I W. Universal approximation using radial-basis-function networks[J].Neural Comp.1991, 3(3):246-257[10]Park J, Sanberg I W. Approximation and radial-basis-function networks[J].Neural Comp.1993, 5(4):305-316
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出版歷程
  • 收稿日期:  1995-12-04
  • 修回日期:  1996-08-04
  • 刊出日期:  1997-07-19

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