The adaptive fuzzy classification system is established by the use of various fire/non-fire data configurations (readily obtained from the Taiwan National Fire Administration Ministry) and from this builds a set of fuzzy rules. Next, two grey-prediction models are set up, one for each sensor. Each GM system interprets the ongoing changes in the dynamic behavior of its respective sensor. Raw data trends, which indicate possible fire situations, i., rapid increase or a continued rise in smoke or temperature slope, result in higher GM predictive output values than the equivalent raw sensor output values. Thus, incorporation of GM between sensor and computer anticipates future sensor values, allowing the system to make fire alarm response before actual alarm conditions, without increasing susceptibility to false alarms. The algorithm in this study is modified from Nozaki, (1996, 238) and Ishibuchi, (1999, 1040). The modified algorithm is used to classify situations as fire or non-fire and consists of three procedures: (1) an automatic procedure for generation of fuzzy rules; (2) a classification procedure; (3) a fuzzy rule self-learning procedure. First, the fuzzy system is established by inputting data for various fire conditions (i. hot smokeless fire, smoky cool fire, etc.) and generating a fuzzy rule base. . Reduction of the Probability of False Alarms and Shorten the Time between Fire Onset and Alarm Response in Shipboard Fires.
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