计算机工程 ›› 2019, Vol. 45 ›› Issue (12): 281-288.doi: 10.19678/j.issn.1000-3428.0053285

• 开发研究与工程应用 • 上一篇    下一篇

基于改进花朵授粉算法的极限学习机模型

邵良杉, 兰亭洋, 李臣浩   

  1. 辽宁工程技术大学 系统工程研究所, 辽宁 葫芦岛 125105
  • 收稿日期:2018-12-03 修回日期:2019-01-15 发布日期:2019-01-22
  • 作者简介:邵良杉(1961-),男,教授、博士、博士生导师,主研方向为数据挖掘、矿业系统工程;兰亭洋、李臣浩,硕士。
  • 基金项目:
    国家自然科学基金(71371091)。

Extreme Learning Machine Model Based on Improved Flower Pollination Algorithm

SHAO Liangshan, LAN Tingyang, LI Chenhao   

  1. System Engineering Institute, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Received:2018-12-03 Revised:2019-01-15 Published:2019-01-22

摘要: 为提高瓦斯突出风险预测的准确率和效率,在极限学习机(ELM)模型的基础上构建预测模型ACFPA-ELM。采用核线性鉴别分析(KLDA)对瓦斯突出样本数据进行特征抽取,利用代价敏感思想修正ELM适应度函数,同时将Tent混沌搜索和自适应算子引入花朵授粉算法(FPA)中,优化ELM的初始输入权值和阈值,从而提高对瓦斯突出风险的预测能力。实验结果表明,相较于经典的SVM、BP和ELM单一预测模型以及改进的FPA-ELM和PSO-ELM复合预测模型,ACFPA-ELM模型在瓦斯突出风险预测的准确率、预测一致性以及运行效率方面均具有明显的优势。

关键词: 瓦斯突出, 花朵授粉算法, 极限学习机, 核线性鉴别分析, 混沌映射

Abstract: In order to improve the accuracy and efficiency of gas outburst risk prediction,this paper proposes a prediction model ACFPA-ELM based on Extreme Learning Machine(ELM).First,this paper adopts Kernel Linear Discriminant Analysis(KLDA) to extract the features of gas outburst sample data.Then,this paper utilizes the cost sensitive ideas to modify ELM fitness function.At the same time,the Tent chaotic search and adaptive operator are introduced into the Flower Pollination Algorithm(FPA) to optimize the initial input weight and threshold of the ELM,thus improving the prediction ability for gas outburst risk.Experimental results show that,compared with the classic SVM,BP and ELM single prediction models,as well as the improved FPA-ELM and PSO-ELM composite prediction models,the proposed model is superior in the accuracy,consistency and efficiency of gas outburst risk prediction.

Key words: gas outburst, Flower Pollination Algorithm(FDA), Extreme Learning Machine(ELM), Kernel Linear Discriminant Analysis(KLDA), chaos mapping

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