Extended belief rule base inference method based on the Hash index

被引：0
|
作者：
Liu W. [1 ]
Xiao C. [1 ]
Fu Y. [1 ]
机构：
[1] College of Mathematics and Computer Science, Fuzhou University, Fuzhou
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2019年 / 46卷 / 02期
关键词：
Evidential reasoning; Extended belief rule base; Index optimization; Locality sensitive Hashing;
D O I：
10.19665/j.issn1001-2400.2019.02.024
中图分类号：
学科分类号：
摘要：
Since the extended belief rule base needs to iterate by all the unordered rules in the inference process, it will result in a low efficiency of the belief rule base in system inference with a large number of rules. Therefore, this paper proposes to use the Locality Sensitive Hashing algorithm to index the confidence rule. First, Locality Sensitive Hashing is used to generate special locality sensitive hash value for all the rules in the Extended belief rule base and the hash value can keep the similarity between the original rules, so that similar rules have a greater probability of obtaining the same index value. Then, by processing the input data, we find the rules that are adjacent to the input data in the index table, and selectively activate these rules, thus improving the system's inference efficiency. Finally, by choosing a nonlinear function fitting experiment and a simulation experiment on oil pipeline leak to the detection Extended belief rule base system based on the Locality Sensitive Hashing index, experimental results show that the Locality Sensitive Hashing algorithms can effectively optimize the Extended belief rule base system inference efficiency and improve the accuracy of the output results. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：145 / 151
页数：6
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