基于贝叶斯决策模型的火灾报警模式识别系统应用研究

针对火灾报警系统中探测器在域值附近由于各种原因出现高漏报率和误报率的情况,提出了将探测器的量测判断作为一种模式进行决策分类.在分类过程中采用贝叶斯决策的方法,将报警的模式分类简化为离散情形下的二值分类的研究,同时依据分类平均风险最小的原则给出了分类的决策函数.实验表明本文提出的解决方法在减少火灾报警系统中的误报率和漏报率方面找到了一个新的方法.结论表明探测器临界局部取值采用模式分类的方法可以达到了比较好的效果.

The research of the pattern recognition in fire alarm system based on Bayes decision-making model

Currently in fire alarm system, detector bring on high failing alarm rate and high mistaking alarm rate within nearby territory value because of some kind of reason. In this article, a solution is proposed that the detector gauging judgment taken a kind of pattern to carry on the decision-making classification. The Bayes decision-making method is used in the classified process. Pattern classification of alarm is simplified to two values classification in discrete condition, at the same time the classification decision-making function is produced based on the principle that the classification average risk is the smallest. The experiment indicates that new solution is found in this article that it can effectually reduce failing alarm rate and mistaking alarm rate. The conclusion indicated that using the pattern classification method is possible to achieve the quite good effect in the detector critical partial value.