Abstract:The Support Vector Machine (SVM) is a widely used tool in
classification problems, but the classification performance of
Support Vector Machine (SVM) largely depends on the choice of its
relevant parameters. This paper proposes a model of Support Vector
Machine (SVM) classification based on Cell-like Membrane computing
Optimization algorithm (CMO-SVM). In the model, the parameters of
Support Vector Machine (SVM) (cost parameter $C$ and RBF kernel
parameter $\sigma$) are optimized by cell-like membrane computing
optimization algorithm for the sake of getting the best combination
parameters of SVM for classification. This method overcomes the
insufficiency of the conventional method which converged to local
optimum, at the same time also has the advantages of good
robustness, fast convergence speed and obtains the global optimal
solution. Finally, to show the applicability and superiority of the
proposed algorithm, the method is employed to identify abnormal
signal of c-band radio (including radar, jammer, single carrier and
single frequency point). Compared with Genetic Algorithm-based SVM
(GA-SVM), Simulated Annealing algorithm-based SVM (SA-SVM), Ant
Colony algorithm-based SVM (AC-SVM), the proposed model performs
best for the four abnormal signal.
Meng Li;Liangzhong Yi;Zhisheng Gao;Zheng Pei;Hong Peng. Support Vector Machine (SVM) Based on Membrane Computing Optimization and the Application for C-band Radio Abnormal Signal Identification[J]. , 2014, 11(11): 3683-3693.
Meng Li;Liangzhong Yi;Zhisheng Gao;Zheng Pei;Hong Peng. Support Vector Machine (SVM) Based on Membrane Computing Optimization and the Application for C-band Radio Abnormal Signal Identification. , 2014, 11(11): 3683-3693.