| 适用于数据分类的极限学习机优化算法 |
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| 引用本文： | 吴亚榕,王 欢,李键红.适用于数据分类的极限学习机优化算法[J].教育技术导刊,2019,18(6):10-13. |
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| 作者姓名： | 吴亚榕 王 欢 李键红 |
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| 作者单位： | 1.仲恺农业工程学院 仲恺科技孵化园；2.仲恺农业工程学院 网络与现代教育技术中心，广东 广州 510225；3.广东外语外贸大学 语言工程与计算实验室，广东 广州 510006 |
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| 基金项目： | 广东省科技计划项目（2016A020210131）；广东省自然科学基金项目（2017A030310618）；广东省哲学社会科学“十三五”规划项目（GD17XGL47） |
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| 摘 要： | 针对极限学习机参数优化问题，提出量子遗传算法优化极限学习机的方法（QGA-ELM）。在该方法中，对ELM的输入权值和隐含层阈值采用量子比特编码，并将其映射为QGA的染色体，QGA的适应度函数为对应ELM的分类精度；通过QGA的量子旋转门优化出输入权值与隐含层阈值，以此训练出分类精度更高的ELM，从而改善ELM的泛化性能。通过ELM和QGA-ELM对数据集的仿真结果对比表明，QGA-ELM有效地提升了ELM网络的分类精度。
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| 关 键 词： | 极限学习机 量子遗传算法 量子旋转门 分类精度 |
| 收稿时间： | 2019-05-09 |
Extreme Learning Machine Optimization Algorithm for Data Classification |
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| WU Ya-rong,WANG Huan,LI Jian-hong.Extreme Learning Machine Optimization Algorithm for Data Classification[J].Introduction of Educational Technology,2019,18(6):10-13. |
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| Authors: | WU Ya-rong WANG Huan LI Jian-hong |
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| Institution: | 1.Zhongkai Technology Incubator，Zhongkai University of Agriculture and Engineering；2.Network and Modern Education Technology Center，Zhongkai University of Agriculture and Engineering，Guangzhou 510225，China；3.Laboratory of Language Engineering and Computing，Guangdong University of Foreign Studies，Guangzhou 510006，China |
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| Abstract: | In order to optimize the parameters of traditional extreme learning machine （ELM）， a new ELM optimized by quantum genetic algorithm （QGA-ELM） was proposed. In this method， the input weights and hidden layer threshold vectors of the ELM were encoded by quantum bits and mapped to chromosomes of QGA， and the fitness function of QGA is the classification accuracy of the corresponding ELM. The input weights and hidden layer threshold vectors optimized by quantum rotation gate were used to train the ELM with higher classification accuracy， thereby improving the generalization performance of ELM. Comparing the simulation results of QGA-ELM and ELM， we draw the conclusion that QGA can effectively improve the classification accuracy of ELM network. |
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| Keywords: | extreme learning machine quantum genetic algorithm quantum rotation gate classification accuracy |
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