| 新冒落带高度算法FOA-SVM预计模型 |
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| 引用本文： | 尹志辉,孙邦达. 新冒落带高度算法FOA-SVM预计模型[J]. 河北化工, 2014, 0(6): 10-13 |
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| 作者姓名： | 尹志辉 孙邦达 |
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| 作者单位： | 沈煤集团沈焦公司西马煤矿;浙江工商大学统计与数学学院; |
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| 摘 要： | 针对顶板冒落带高度问题提出新的预计模型,通过搜集众多矿井的实测数据,在支持向量机理论基础上建立预计模型。采用果蝇优化算法对预计模型进行优化训练,建立FOA-SVM预计模型,利用实测数据对模型的预计结果进行检验,预计结果较为准确,比PSO-SVM模型和GA-SVM模型结果稳定性好计算精度高。
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| 关 键 词： | 冒落带高度 支持向量机SVM 果蝇算法FOA 模型优化 |
New Roof Falling Highly Algorithm FOA-SVM Prediction Model |
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| YIN Zhi-hui;SUN Bang-da. New Roof Falling Highly Algorithm FOA-SVM Prediction Model[J]. Hebei Chemical Engineering and Industry, 2014, 0(6): 10-13 |
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| Authors: | YIN Zhi-hui SUN Bang-da |
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| Affiliation: | YIN Zhi-hui;SUN Bang-da;Xima Mine,Shenyang Coal Shenyang Coke Corporation Ltd.;School of statistics and mathematics,Zhejiang Gongshang University; |
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| Abstract: | Based on the roof fall height problem, the new model was put forward, by collecting numerous experimental data of coal mine, based on support vector machine （SVM） theory, the expoeted model was settled. Using drosophila optimization algorithm optimized the new model training, the FOA - the SVM prediction model was established, using the measured data, the expected results of model test was expected more accurately, which had better stability calculation and more accuracy than PSO - SVM model and GA - SVM model results. |
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| Keywords: | roof fall height support vector machine SVM flies algorithm FOA model optimization |
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