A Self-adaptive Evolutionary Negative Selection Approach for Home Anomaly Events Detection
A Self-adaptive Evolutionary Negative Selection Approach for Home Anomaly Events Detection
复制标题
DOI：
10.1007/978-3-540-74829-8_40
复制
发表时间：
2007-09
期刊：
影响因子：
--
通讯作者：
Huey-Ming Lee;Ching-Hao Mao
中科院分区：
文献类型：
--
作者：
Huey-Ming Lee;Ching-Hao Mao
在本研究中，我们将自适应进化否定选择方法应用于家庭异常事件检测。否定选择算法，又称穷举检测器生成算法，是针对各种异常检测问题而提出的，其概念源于人工免疫系统。以家庭异常控制规则为检测器，在环境因素发生变化时，将模糊遗传算法应用于自适应信息家电控制系统。该方法对家庭环境因素的变化具有自适应性和增量性。通过对异常温度检测的实现，使信息家电控制系统具有更强的安全性、适应性和定制化。
In this study, we apply the self-adaptive evolutionary negative selection approach for home abnormal events detection. The negative selection algorithm, also termed the exhaustive detector generating algorithm, is for various anomaly detection problems, and the concept originates from artificial immune system. Regarding the home abnormal control rules as the detector, we apply fuzzy genetic algorithm for self-adaptive information appliances control system, once the environment factors change. The proposed approach can be adaptive and incremental for the home environment factor changes. Via implementing the proposed approach on the abnormal temperature detection, we can make the information appliance control system more secure, adaptive and customized.