To reduce false fire alarms, combining with the character of fire signal, a kind of intelligent fire detection system of multi-sensor information fusion based on fuzzy neural network is proposed in this paper . This fire detector fuses three sensor data including temperature, smoke and CO air which have obvious character in fire and fire probability can be obtained by intelligent arithmetic of fuzzy neural network. As a result, The accuracy of the fire detection is improved effectively and the feasibility and validity of the system are proved by the simulation effects.
0 Foreword
The purpose of fire detection technology is to make accurate judgments of the fire and to predict the fire in the early time, so that people's lives and property can be protected. Based on the monitoring of physical phenomena such as light, smoke, heat, the traditional fire detection usually monitors one kind of physical quantity and establishes a certain threshold value as the criterion for the fire. In practice, it is discovered that fire monitoring, based on a certain physical quantity and threshold value, is often inevitably influenced by a certain similar environmental factors influence which causes false alarm.
1 Multi-sensor Data Fusion Fire Detection System
For any kind of detective object, using only one kind of information to reflect its condition is not complete. Only through getting, integrating and using various multi-dimensional information of the same object, it can detect the fire accurately and early. In view of the fact that unit fire detection technology has been unable to meet the needs of real fire alarm, the system uses multiple information fusion fire detection, which is not the simple combination of the fire detectors original single parameter, but the implementation of multiple simultaneous detection, extraction of useful and accurate information. According to different types of fire parameters, it applies intelligent algorithms, fuses the fire parameters of multi-sensor fusion, and determines whether there is a fire hazard. It overcomes the limitations of a single sensor, and effectively improves the ability of identifying real or false fires.
Under normal circumstances, CO is extremely low in the air. Only by burning massive CO can be produced, which causes the density of CO in the air to increase sharply. Thus the detection of CO gas will be in large part reflects whether the combustion phenomenon happens or not. The occurring of fire is often accompanied with the elevation of temperature and the enlargement of smoke density, so the system of fire detectors uses 3-layer structure of multi-sensor fusion, selects temperature sensors, smoke sensors, gas sensors, the temperature signal, smoke concentration and the CO concentration as the fire detection signal.
2 Fuzzy Neural Network
Applying fuzzy neural network to fire detection information processing can greatly improve the timeliness and accuracy of fire detection, and reduce the rate of false alarm.This system uses fuzzy neural network as shown in Figure 1. Before and after the neural network in the system is in series with the fuzzy system, in order to facilitate the procession of neural network, the smog density signal from the environment examination, the temperature signal as well as the gas signal through the signal pretreatment should be normalized, and sends these three normalized values into the fuzzy system, uses trigonometric functions for transformation, and obtains three degree of membership and the feedback signal of neural network as the neural network input.
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