Research On Fire Alarm System Based On Hybrid Artificial Bee Colony Optimization Neural Network

As a common hidden danger in daily life,fire hidden in many areas of life,will bring great threat to people’s lives and property.But at present,there are some technical problems in the fire alarm system.First,only using the detection signal of a single sensor to reflect the fire information can not reflect the fire in all directions.Secondly,it is easy to be interfered by the environmental noise if the original data monitored by the sensor is taken as the basis to judge the fire.Third,it is relatively simple to judge the occurrence of a fire by adopting a fixed threshold directly,but in the face of such a complex and changeable fire accident,it is difficult to fix a reasonable threshold to classify the fire,resulting in missing or false alarm.Fourthly,most of the alarm systems only have the function of monitoring environmental information and giving fire alarm,which can not easily control the whole system after the fire.Therefore,it is necessary to design an intelligent and humanized multi-sensor fire alarm system which has strong anti-interference ability and can deal with complex environment accurately and reliably.According to the demand of high-speed railway fire alarm system,this paper builds the hardware platform of fire alarm system.The main contents are as follows(1)For the problem that high-speed railway environment is narrow and needs more detectors,this paper designs a two bus interface circuit based on clip protocol,which can realize the communication between detector and information receiving and processing module,and supply power for detector module.(2)Then,a set of simple can communication instructions is designed on the hardware platform,and the can communication driver between each sub module is realized by singlechip microcomputer.IIC and UART driver are designed for the display module to realize the function of the display control system.USB host and SPI driver are designed for the storage and download module to realize the function of system data storage and download Yes.(3)For the interference problem of environmental noise to the monitoring value of the detector,a Kalman filter is designed to filter the original monitoring value of the detector.(4)For the complex fire environment,it is not easy to classify the fire,missing and false alarm.In this paper,BP,GRNN and PNN neural networks are designed respectively to classify the fire data by using the excellent prediction and classification ability of artificial neural network for uncertain problems.Then,the classification accuracy and convergence speed of each neural network are compared and discussed.Finally,BP neural network is selected as the fire prediction model Classifier.(5)For the problem of poor robustness caused by the randomization of initial parameters in BP neural network,which is easy to fall into local optimum and over fitting,this paper introduces artificial bee colony algorithm into BP neural network combined with the excellent optimization ability of artificial bee colony algorithm.Then,aiming at the problem that the standard artificial bee colony algorithm does not use the global optimal solution and has weak development ability,this paper designs a hybrid artificial bee colony algorithm combined with particle swarm optimization algorithm,which can significantly improve the optimization accuracy and speed.Finally,this paper introduces the designed hybrid artificial bee colony algorithm into the designed BP neural network fire classifier to optimize its initial parameters,and designs a hybrid artificial bee colony BP artificial neural network for fire classification,which improves the accuracy and robustness.First of all,the design of the fire alarm system can be verified.Through the oscilloscope to observe the communication waveform of the two bus,it is consistent with the clip communication protocol.The detector can monitor the environmental information and give an alarm in case of fire,which reduces the wiring resources and the difficulty of maintenance.Then the control module and interaction module are verified.The results show that the indicator module can achieve the pre designed function,the display module can manage the system more humanized,and the storage and download module can save the historical data normally.The Kalman filter designed in this paper is verified by MATLAB,and the results show that the error can be reduced by 60% ～ 70% after filtering.Finally,the classification effect of the hybrid artificial bee colony BP neural network designed in this paper is verified by MATLAB.The results show that the classification accuracy is as high as 99.8333%,which is improved by 2.6%compared with the direct use of BP neural network.To sum up,the verification results show that the fire alarm system designed in this paper can reliably monitor the high-speed railway fire,while taking into account the anti-interference ability,intelligent classification and humanized management to ensure the safety of the train.