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Fuzzy rule mining of fault diagnosis based on binary harmony search algorithm

机译：基于二元和声搜索算法的故障诊断模糊规则挖掘

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摘要

Extraction of fuzzy rules from numerical test data in practice is significant for intelligence fault diagnosis of complex system. But the traditional methods of Ant-Miner and CN2 algorithms have many disadvantages such as understanding difficultly, realize complexly, adjustable parameters too many, and the predictive accuracy of mined rules is not good. To solve these problems, in this paper, a new rule mining method based on binary harmony search algorithm (BHSA-Miner) is proposed, which set up heuristic function to improve the performance by rebuild the harmony memory. Simulation and application results demonstrate that compared with the Ant-Miner and CN2 algorithms, BHSA-Miner is able to acquire high quality of fuzzy rules under the same amount of rules and terms. This proved the validity of the BHSA-Miner.
机译：从实践中的数值测试数据提取模糊规则对于复杂系统的智能故障诊断是显着的。但是，蚂蚁矿工和CN2算法的传统方法有许多缺点，例如理解，实现复杂，可调节的参数太多，并且开采规则的预测准确性不好。为了解决这些问题，提出了一种基于二进制和声搜索算法（BHSA-MINER）的新规则挖掘方法，该方法设定了通过重建和谐记忆来提高性能的启发式功能。仿真和应用结果表明，与蚂蚁和CN2算法相比，BHSA-Miner能够在相同数量的规则和条款下获得高质量的模糊规则。这证明了Bhsa-Miner的有效性。

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