CAJ | 학술논문

研究将贝叶斯决策应用于自适应神经-模糊推理系统（ANFIS）的视频烟雾检测系统。提取视频烟雾特征，通过减法聚类和混合学习算法，确定并优化得到ANFIS实例，引入贝叶斯决策对ANFIS输出进行检测判别。仿真实验表明，ANFIS比其他烟雾检测算法具备更好的检测性能，而基于最小风险的贝叶斯决策可进一步提高检测率和降低虚警率，能更好地满足实际应用的需求。
연구장패협사결책응용우자괄응신경-모호추리계통（ANFIS）적시빈연무검측계통。제취시빈연무특정，통과감법취류화혼합학습산법，학정병우화득도ANFIS실례，인입패협사결책대ANFIS수출진행검측판별。방진실험표명，ANFIS비기타연무검측산법구비경호적검측성능，이기우최소풍험적패협사결책가진일보제고검측솔화강저허경솔，능경호지만족실제응용적수구。
The Bayesian decision method is studied to further improve the performance of detecting video smoke using Adaptive Neuro-Fuzzy Inference System(ANFIS). Smoke features are extracted from video sequences. The subtractive clustering and hybrid learning rules are used to train ANFIS. Detection outputs are determined by performing proposed Bayesian decision rules on the outputs of ANFIS. Experimental results show that the detection performance of ANFIS is better than that of other smoke detection algorithms, and the introduction of minimum risk-based Bayesian decision rules further increases the detection rate and decreases the false alarm rate, which is more valuable for practical applications.