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两篇会议论文帮忙查检索号，谢谢 已有1人参与
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论文题目1)study on extension expert system based on production rule and its application;2)application of rbf neural network based on enn2 clustering in fault diagnosis，如果有检索麻烦发到我邮箱307751810@QQ.com [ 发自手机版 http://muchong.com/3g ] |
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2楼2015-01-13 22:04:33
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wentianzhu: 金币+10, ★★★★★最佳答案, 10 2015-01-13 22:28:36
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第一篇没有检索；第二篇如下： Accession number: 201446203519 Title: Application of RBF neural network based on ENN2 clustering in fault diagnosis Authors: Wen, Tianzhu1 Email author wentianzhu1987@aliyun.com; Xu, Aiqiang1; Liu, Chunxia1; Li, Nan2 Author affiliation: 1 Naval Aeronautical and Astronautical University, Yantai, China 2 P.L.A., Beijing, China Corresponding author: Wen, Tianzhu Source title: Proceedings - 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014 Abbreviated source title: Proc. - Int. Conf. Intelligent Hum.-Mach. Syst. Cybernetics, IHMSC Volume: 2 Issue date: September 25, 2014 Publication year: 2014 Pages: 71-74 Article number: 6911448 Language: English ISBN-13: 9781479949557 Document type: Conference article (CA) Conference name: 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014 Conference date: August 26, 2014 - August 27, 2014 Conference location: Hangzhou, China Conference code: 108130 Sponsor: Zhejiang Provincial Natural Science Foundation of China Publisher: Institute of Electrical and Electronics Engineers Inc. Abstract: Radial basis function (RBF) neural network is widely used in engineering with its powerful advantage in solving nonlinear problems. But the number of hidden layer as well as the center and standard deviation of radial basis function are difficult to get, so RBF neural network based on ENN2 is proposed to solve the fault diagnosis problem. Firstly, the structure of RBF neural network is introduced, afterwards, the learning algorithm of RBF neural network is analyzed, the center and standard deviation of RBF in hidden layer are obtained by clustering method of extension neural network type 2(ENN2), meanwhile the weight matrix between hidden layer and output layer are calculated by generalized inverse method. Ultimately, the method is used to solve fault diagnosis problem, the results show that it has the advantages of simple structure, fast learning speed and high diagnostic accuracy. Number of references: 13 Main heading: Failure analysis Controlled terms: Unsupervised learning Uncontrolled terms: ENN2 clustering - Extension theory - RBF Neural Network Classification code: 421 Strength of Building Materials; Mechanical Properties - 731.5 Robotics - 921 Mathematics DOI: 10.1109/IHMSC.2014.120 Database: Compendex Compilation and indexing terms, © 2015 Elsevier Inc. |
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