The grey model (GM) has been successfully and widely used (Kuo, 2001, 55). GM is often applied to prediction in time-varying non-linear systems. Kuo and Wu (2001, 59) used GM to predict the deformation of thin ship panels. Yuan et al. (2000, 13) proposed a method based on grey theory to predict gas-in-oil concentrations in an oil-filled transformer. This present paper improves upon the above studies by endowing a dual-sensor fuzzy detection system with GM predictive ability, thereby shortening alarm response time and increasing alarm accuracy. 1 presents a diagram of the proposed system, showing a temperature sensor, a smoke sensor, individual GM circuits for each sensor and a fuzzy classification system.
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 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) 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.
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