基于粒计算的极限学习机模型设计与应用

doi:10.11896／j.issn.1002-137X.2018.10.012

摘要/Abstract

摘要： 在数据智能处理中属性重要度差异很大且具有高度非线性的特征,在这种情况下直接应用机器学习进行建模处理往往很难获得问题的有效解。针对此问题,文中探索了基于粒计算的属性重要度的排序方法且结合排序结果应用二元关系实现粒层划分算法;应用极限学习机对不同划分获得的粒层空间进行学习,进而对不同粒层空间的学习结果进行对比分析,从而获得最优划分与粒层;此外,将提出的粒度极限学习机模型应用于空气质量的预报问题,不仅加快了预报速度,而且获得的结果与实际预测高度吻合,实证了粒度极限学习机模型的有效性和可靠性。

关键词: 二元关系, 极限学习机, 粒层空间, 粒计算

Abstract: The importance of attributes in data intelligence processing is not only different from each other,but also highly nonlinear.In such case,it is difficult to obtain effective solutions to the problem by applying machine learning directly.In order to solve this problem,the granularity-based ranking method of attribute importance and the application of the ranking results in binary relationship were explored to perform the granular partitioning algorithm.Then,this paper applied extreme learning machine to granular layer space.The learning results in the layer space were compared and analyzed to obtain the optimal partition and granular layer.In addition,the particle size extreme learning machine model proposed in this paper was applied to the air quality forecasting problem,not only accelerating the forecasting speed,but also being consistent with the actual forecasting,thus empirically proving the validity and reliability of extreme learning machine model.

Key words: Binary relationship, Extreme learning machine, Granular computing, Granular space

中图分类号:

TP391

陈丽芳, 代琪, 付其峰. 基于粒计算的极限学习机模型设计与应用[J]. 计算机科学, 2018, 45(10): 59-63. https://doi.org/10.11896／j.issn.1002-137X.2018.10.012

CHEN Li-fang, DAI Qi, FU Qi-feng. Design and Application of Extreme Learning Machine Model Based on Granular Computing[J]. Computer Science, 2018, 45(10): 59-63. https://doi.org/10.11896／j.issn.1002-137X.2018.10.012

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