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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kexin Jia Youxin Lu |
| Copyright Year | 2007 |
| Description | Author affiliation: Univ. of Electron. Sci. & Technol. of China, Chengdu (Kexin Jia; Youxin Lu) |
| Abstract | To make the modulation classification system more suitable for signals in a wide range of signal noise rate (SNR), a novel method of designing combined classifier based on fuzzy integral and multi-class support vector machines (MSVM) is presented in this paper. The method employs multi-class support vector machines classifiers and fuzzy integral to improve recognition reliability. Experimental results illustrate that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 98.2% when SNR is not lower than 5 dB). |
| Starting Page | 947 |
| Ending Page | 950 |
| File Size | 1931542 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424414734 |
| DOI | 10.1109/ICCCAS.2007.4348204 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-07-11 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Fuzzy sets Design methodology Support vector machine classification Training data Signal processing Electronic mail Signal design Fuzzy systems Signal to noise ratio |
| Content Type | Text |
| Resource Type | Article |
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