计算机科学 ›› 2015, Vol. 42 ›› Issue (5): 88-93.doi: 10.11896/j.issn.1002-137X.2015.05.018

• 2014' 数据挖掘会议 • 上一篇    下一篇

专家干预下置信规则库参数训练的差分进化算法

王韩杰,杨隆浩,傅仰耿,吴英杰,巩晓婷   

  1. 福州大学数学与计算机科学学院 福州350116,福州大学数学与计算机科学学院 福州350116,福州大学数学与计算机科学学院 福州350116,福州大学数学与计算机科学学院 福州350116,福州大学经济与管理学院 福州350116
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金青年项目(61300026,4),国家杰出青年科学基金项目(70925004),国家自然科学基金面上项目(71371053),福建省自然科学基金项目(2015J01248),福建省教育厅A类科技项目(JA13036),福州大学科技发展基金项目(2014-XQ-26)资助

Differential Evolutionary Algorithm for Parameter Training of Belief Rule Base under Expert Intervention

WANG Han-jie, YANG Long-hao, FU Yang-geng, WU Ying-jie and GONG Xiao-ting   

  • Online:2018-11-14 Published:2018-11-14

摘要: 传统关于置信规则库参数训练模型的求解主要采用FMINCON函数及群智能算法,但在算法设计中并未涉及所有的置信规则库参数,且缺少必要的专家干预。为解决这些问题,首先在现有参数模型的基础上进一步扩宽参与参数训练的置信规则库参数,然后设计出符合思维逻辑的专家干预的约束条件,最后结合差分进化算法提出具有更高收敛精度的置信规则库参数训练方法。在实验分析中,首先在多极值函数的实例中分析该方法的有效性,再在输油管道检漏的实例中检验专家干预的合理性及对比现有的其他参数训练方法。实验结果表明,该方法是有效可行的。

关键词: 置信规则库,参数训练,差分进化算法,专家干预

Abstract: Traditionally,FMINCON function and swarm intelligence algorithms are adopted to search for solutions of training model about the parameters of belief rule base(BRB) system.However,all the parameters of BRB system are not involved in these training processes,and there is a lack of the necessary intervention from the experts in these algorithms.In view of these,firstly,the parameters of BRB system were further broadened on the basis of the existing mo-dels.Then,the logical constraints under the expert intervention were designed.Finally,the new training method for BRB system with better convergent accuracy which is combined with the differential evolutionary algorithm was proposed.In the experiment analysis of the new approach,the effectiveness was validated via the example of a multi-extreme function,and the rationality was examined under the expert intervention in the example of pipeline leak detection by comparing with the existing approaches of parameter training.The results show that the proposed method is effective and feasible.

Key words: Belief rule base,Parameter training,Differential evolutionary algorithm,Expert intervention

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