摘要
通过对人工免疫系统中阴性选择算法机理的分析,定义了连续相似度与背离度,提出了一种可变模糊匹配阴性选择免疫算法.算法通过调整匹配阈值的方法降低黑洞数量;利用模糊思想,实现了具有一定连续相似度的模糊匹配,模糊程度可控;为了消除检测器间的冗余,提高检测器集的检测效率,算法在模糊匹配的基础上,生成了有效检测器集.仿真实验表明,可变模糊匹配阴性选择算法生成的成熟检测器检测范围较大,空间覆盖率明显提高,黑洞数量大幅下降,算法具有较强的鲁棒性.
This paper analyzed the negative selection algorithm mechanism in an artificial immune system,defined continuous similarity and deviation,and put forward an adjustable fuzzy matching negative selection immune algorithm.The algorithm clearly reduced the number of holes through adjusting the matching threshold,and used a fuzzy idea to realize fuzzy matching with continuous controlled similarity.In order to eliminate the redundancy phenomenon between detector sets and increase the detecting efficiency,an effective detector set was created on the basis of fuzzy matching.The simulation results show that the mature detector generated by the adjustable fuzzy matching negative selection algorithm can detect data in a larger range and the space coverage ratio is noticeably increased.Also,the number of holes clearly declines,and the algorithm has better robustness.
出处
《智能系统学报》
2011年第2期178-184,共7页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金资助项目(60704004)
黑龙江省博士后基金资助项目(LBH-Z09216)
中央高校基本科研业务费专项基金资助项目(HEUCF00401)
关键词
阴性选择
连续相似度
模糊匹配
黑洞
有效检测器集
negative selection
continuous similarity
fuzzy matching
holes
effective detector sets
