| RBF人工神经网络拓扑结构定义与解的唯一性证明 |
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| 引用本文： | 陈廷勇,殷树友,林和平. RBF人工神经网络拓扑结构定义与解的唯一性证明[J]. 东北师大学报(自然科学版), 2009, 41(3) |
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| 作者姓名： | 陈廷勇 殷树友 林和平 |
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| 作者单位： | 陈廷勇,殷树友(长春金融高等专科学校计算机系,吉林,长春,130022);林和平(东北师范大学计算机学院,吉林,长春,130117) |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 以RBF人工神经网络为例,通过引入透明人工神经元的定义,提高了人工神经网络拓扑结构表述方面的一致性,证明了人工神经网络解的唯一性与确定性,提出了训练算法的非随机性和学习速率的最佳选择.
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| 关 键 词： | 人工智能(AI) 人工神经网络(ANN) 径向基(RBF) |
RBF artificial neural network topology definition and proof of uniqueness of solution |
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| CHEN Ting-yong,YIN Shu-you,LIN He-ping. RBF artificial neural network topology definition and proof of uniqueness of solution[J]. Journal of Northeast Normal University (Natural Science Edition), 2009, 41(3) |
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| Authors: | CHEN Ting-yong YIN Shu-you LIN He-ping |
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| Affiliation: | CHEN Ting-yong1,YIN Shu-you1,LIN He-ping2(1.Department of Computer,Changchun Finance College,Changchun 130022,China,2.College of Computer,Northeast Normal University,Changchun 130117,China) |
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| Abstract: | In this paper,it takes the radial-basis function artificial neural network(RBFANN)as an example.It is propose that the definition of transparent artificial neurons,enhance the consistency in describing the network architecture.Then it proves the uniqueness of the solution of ANN,put forward a non-randon training algorithm. |
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| Keywords: | artificial intelligence(AI) artificial neural network(ANN) radial-basis function(RBF) |
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