Computer Science ›› 2015, Vol. 42 ›› Issue (5): 88-93.doi: 10.11896/j.issn.1002-137X.2015.05.018

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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

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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