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Weighted Learning for Feedforward Neural Networks

Weighted Learning for Feedforward Neural Networks
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摘要 In this paper, we propose two weighted learning methods for the construction of single hidden layer feedforward neural networks. Both methods incorporate weighted least squares. Our idea is to allow the training instances nearer to the query to offer bigger contributions to the estimated output. By minimizing the weighted mean square error function, optimal networks can be obtained. The results of a number of experiments demonstrate the effectiveness of our proposed methods. In this paper, we propose two weighted learning methods for the construction of single hidden layer feedforward neural networks. Both methods incorporate weighted least squares. Our idea is to allow the training instances nearer to the query to offer bigger contributions to the estimated output. By minimizing the weighted mean square error function, optimal networks can be obtained. The results of a number of experiments demonstrate the effectiveness of our proposed methods.
作者 Rong-Fang Xu Thao-Tsen Chen Shie-Jue Lee
机构地区 Department of Electrical Engineering
出处 《Journal of Electronic Science and Technology》 CAS 2014年第3期299-304,共6页 电子科技学刊（英文版）
基金 supported by the NSC under Grant No.NSC-100-2221-E-110-083-MY3 and NSC-101-2622-E-110-011-CC3 "Aim for the Top University Plan"of the National Sun-Yat-Sen University and Ministry of Education
关键词 Extreme learning machine hybrid learning instance-based learning weighted least squares Extreme learning machine,hybrid learning,instance-based learning,weighted least squares
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献14

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Journal of Electronic Science and Technology
2014年 第3期

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