Improving the generalization performance of RBF neural networks using a linear regression technique期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

Improving the generalization performance of RBF neural networks using a linear regression technique

Authors:	CL Lin JF Wang CY Chen CW Chen CW Yen

Affiliation:	1. Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80441, Taiwan;2. Department of Management Information System, Yung-Ta Institute of Technology and Commerce, 316 Jong Shan, Rd., Lin Luoh, Pingtung 90941, Taiwan;3. Department of Logistics Management, Shu-Te University, Kaohsiung 82445, Taiwan;4. Department of Computer Science, National Pingtung University of Education, No. 4-18, Ming Shen Rd., Pingtung 90003, Taiwan;1. Faculty of Engineering and Natural Sciences, Mechatronics Engineering, Bahcesehir University, 34353 Istanbul, Turkey;2. Department of Mechanical Engineering, Trakya University, 22030 Edirne, Turkey;1. Department of Agricultural Machinery Engineering, Faculty of Agricultural, University of Tabriz, Tabriz, Iran;2. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran;3. Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran, Iran;1. Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan, 243-032, China;2. Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran;3. Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam;1. School of Economics and Management, North China Electric Power University, Beijing 102206, China;2. Department of Information Management, Oriental Institute of Technology, Panchiao, New Taipei 226, Taiwan;1. University of Civil Engineering, 55 Giai Phong Road, Ha Noi, Viet Nam;2. Construction Technical College No. 1, Trung Van, Tu Liem, Ha Noi, Viet Nam

Abstract:	In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram–Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.

Keywords:	Radial basis function Neural network Function approximation Generalization performance Orthogonal least squares
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