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首页> 外文期刊>Journal of Computers >The Chaos Differential Evolution Optimization Algorithm and its Application to Support Vector Regression Machine
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The Chaos Differential Evolution Optimization Algorithm and its Application to Support Vector Regression Machine

机译：混沌差分进化优化算法及其在支持向量回归机中的应用

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The Differential Evolution (DE) population-based algorithm is an optimal algorithm with powerful global searching capability, but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaos Differential Evolution algorithm (CDE) based on the cat map is proposed which combines DE and chaotic searching algorithm. Firstly, the chaotic distributed superiority of the cat map is analyzed in this paper. Secondly, the detailed implementation of CDE is introduced. Finally, the effectiveness of CDE is verified in the numerical tests. The Support Vector Regression machine (SVR) is an effective tool to solve the problem of nonlinear prediction, but its prediction accuracy and generalization performances depend on the selection of parameters greatly. So, the CDE is applied to SVR to build an optimized prediction model called CDE-SVR. Then the new prediction model is applied to the short-time regression prediction of the chaotic time series and the boundary extension of the mechanical vibration signals. The results of the two experiments demonstrate the effectiveness of the CDE-SVR.
机译：基于差分进化（DE）种群的算法是一种具有强大全局搜索能力的最优算法，但通常收敛速度较慢，并且在后期进化阶段表现出较差的搜索能力。提出了一种新的基于猫图的混沌差分进化算法（CDE），该算法结合了DE和混沌搜索算法。首先，分析了猫图的混沌分布优越性。其次，介绍了CDE的详细实现。最后，通过数值测试验证了CDE的有效性。支持向量回归机（SVR）是解决非线性预测问题的有效工具，但其预测精度和泛化性能在很大程度上取决于参数的选择。因此，将CDE应用于SVR，以建立称为CDE-SVR的优化预测模型。然后将新的预测模型应用于混沌时间序列的短时回归预测和机械振动信号的边界扩展。这两个实验的结果证明了CDE-SVR的有效性。

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