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Efficient VLSI Architecture for Training Radial Basis Function Networks
Department of Computer Science and Information Engineering, National Taiwan Normal University,Taipei 116, Taiwan
* Author to whom correspondence should be addressed.
Received: 21 February 2013; in revised form: 11 March 2013 / Accepted: 14 March 2013 / Published: 19 March 2013
Abstract: This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.
Keywords: reconfigurable computing; system on programmable chip; FPGA; radial basis function; fuzzy C-means
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Fan, Z.-C.; Hwang, W.-J. Efficient VLSI Architecture for Training Radial Basis Function Networks. Sensors 2013, 13, 3848-3877.
Fan Z-C, Hwang W-J. Efficient VLSI Architecture for Training Radial Basis Function Networks. Sensors. 2013; 13(3):3848-3877.
Fan, Zhe-Cheng; Hwang, Wen-Jyi. 2013. "Efficient VLSI Architecture for Training Radial Basis Function Networks." Sensors 13, no. 3: 3848-3877.