Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm–support vector machines: HGASVM-Reference-Cited by-同舟云学术

Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm–support vector machines: HGASVM

Published:2009-03 Issue:2 Volume:36 Page:1391-1402
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Avci Engin

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference54 articles.

1. Wavelet applications in medicine;Akay;IEEE Spectrum,1997

2. Intelligent target recognition based on wavelet packet neural network;Avci;Experts Systems with Applications,2005

3. Intelligent target recognition based on wavelet adaptive network based fuzzy inference system;Avci;Lecture Notes in Computer Science,2005

4. Automatic modulation recognition-I;Azzouz,1996

5. Automatic modulation recognition-Il;Azzouz,1996

Cited by 69 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning-based fishing ground prediction for albacore and yellowfin tuna in the Western and Central Pacific Ocean;Fisheries Research;2024-10

2. Comparing the performances of support vector machines, artificial neural network, and a logistic regression model for predicting coronary artery diseases: a cross‑sectional study;2023-08-02

3. Cost-sensitive max-margin feature selection for SVM using alternated sorting method genetic algorithm;Knowledge-Based Systems;2023-05

4. On-Chip Acceleration of RF Signal Modulation Classification With Short-Time Fourier Transform and Convolutional Neural Network;IEEE Access;2023

5. A review of optimization algorithms in SVM parameters;ADVANCES IN MATERIAL SCIENCE AND MANUFACTURING ENGINEERING;2023