Parameter selection of support vector machine based on stepped-up chaos optimization algorithm
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摘要: 针对支持向量机参数选择问题, 以惩罚系数、不敏感系数和RBF核函数中的宽度系数为优化变量, 采用Chebyshev映射代替Logistic映射产生初始混沌序列, 改变原有的搜索公式及增加3次载波, 提出了一种改进的加速混沌优化算法(ISCOA)。将该算法应用于人工数据集和实际数据集中, 并与常规的交叉验证法进行比较。试验结果表明: 在人工数据集中, 采用ISCOA在时间上缩短了至少23.43%, 精度上提高了至少6.31%;在实际数据集中, 预测值更接近实际值, 相对误差均控制在3.13%以下, 该算法具有较高的预测精度和寻优效果。Abstract: In order to analyze the parameter selection of support vector machine (SVM), penalty coefficient, insensitive coefficient and width coefficient in radial basis function (RBF) were used as optimization variables, the former searching formula was changed, and the third searching time was added. A new improved stepped-up chaos optimization algorithm (ISCOA) was proposed by adopting the Chebyshev mapping instead of Logistic mapping to form initial chaos serial. The new algorithm was used in artificial data set and real data set, and was compared with traditional cross validation method. Test result indicates that the running time is cut down at least 23.43%, and the precision improves at least 6.31% by using ISCOA in artificial data set. The predicted value is more close to real value, and the relative errors are controlled under 3.13% in real data set. So ISCOA has higher prediction precision and optimization effect.
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表 1 试验结果比较
Table 1. Comparison of test results
指标 34个样本(δ=0.05) 67个样本(δ=0.10) CV算法 ISCOA CV算法 ISCOA 时间/s 223.56 139.42 321.58 246.25 MSE值 0.002 05 0.001 73 0.001 62 0.001 43 MRE值 1.357 60 1.035 80 0.815 70 0.764 20 
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表 2 货物吞吐量比较
Table 2. Comparison of cargo throughputs
年份 实际值/104 t CV算法 ISCOA 预测值/104 t 相对误差 预测值/104 t 相对误差 2001 8 653 9 021.54 0.042 6 8 923.47 0.031 3 2002 10 638 10 660.49 0.002 1 10 428.26 0.019 7 2003 12 968 12 999.07 0.002 4 12 990.59 0.001 7 2004 15 836 15 688.92 0.009 3 15 951.23 0.007 3 2005 18 492 18 373.91 0.006 4 18 421.81 0.003 8
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表 3 集装箱吞吐量比较
Table 3. Comparison of container throughputs
年份 实际值/104 TEU CV算法 ISCOA 预测值/104 TEU 相对误差 预测值/104 TEU 相对误差 2001 634.1 654.98 0.032 9 648.73 0.023 1 2002 861.4 841.40 0.023 2 873.85 0.014 5 2003 1 128.0 1 116.36 0.010 3 1 136.38 0.007 4 2004 1 455.0 1 448.18 0.004 7 1 450.52 0.003 1 2005 1 808.0 1 837.66 0.016 4 1 789.14 0.010 4
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