Efficient VLSI Architecture for Training Radial Basis Function Networks
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/13/3/3848 |
| _version_ | 1811297837805207552 |
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| author | Wen-Jyi Hwang Zhe-Cheng Fan |
| author_facet | Wen-Jyi Hwang Zhe-Cheng Fan |
| author_sort | Wen-Jyi Hwang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T06:11:06Z |
| format | Article |
| id | doaj.art-c1c905892a2b43248405046b269c814f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:11:06Z |
| publishDate | 2013-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c1c905892a2b43248405046b269c814f2022-12-22T02:59:04ZengMDPI AGSensors1424-82202013-03-011333848387710.3390/s130303848Efficient VLSI Architecture for Training Radial Basis Function NetworksWen-Jyi HwangZhe-Cheng FanThis 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.http://www.mdpi.com/1424-8220/13/3/3848reconfigurable computingsystem on programmable chipFPGAradial basis functionfuzzy C-means |
| spellingShingle | Wen-Jyi Hwang Zhe-Cheng Fan Efficient VLSI Architecture for Training Radial Basis Function Networks Sensors reconfigurable computing system on programmable chip FPGA radial basis function fuzzy C-means |
| title | Efficient VLSI Architecture for Training Radial Basis Function Networks |
| title_full | Efficient VLSI Architecture for Training Radial Basis Function Networks |
| title_fullStr | Efficient VLSI Architecture for Training Radial Basis Function Networks |
| title_full_unstemmed | Efficient VLSI Architecture for Training Radial Basis Function Networks |
| title_short | Efficient VLSI Architecture for Training Radial Basis Function Networks |
| title_sort | efficient vlsi architecture for training radial basis function networks |
| topic | reconfigurable computing system on programmable chip FPGA radial basis function fuzzy C-means |
| url | http://www.mdpi.com/1424-8220/13/3/3848 |
| work_keys_str_mv | AT wenjyihwang efficientvlsiarchitecturefortrainingradialbasisfunctionnetworks AT zhechengfan efficientvlsiarchitecturefortrainingradialbasisfunctionnetworks |