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

Abstract

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

CLC Number:

TP391

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.

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Design and Application of Extreme Learning Machine Model Based on Granular Computing

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