【摘要】 现有的煤矿安全监测系统一般采用实时采集数据的方法,在达到危险限时给出警报。如果能够在达到危险限前给出预警,现场提前采取安全措施,就有可能避免事故的发生。应用神经网络理论,在改进的学习算法的基础上,建立了预测监测数据的BP模型,并通过Matlab实现了仿真验证。通过对监测数据的测试,证实了改进算法和模型的有效性。更多还原

【Abstract】 The existing mine safety monitoring system generally use real-time data collection methods, it will give alarm when reach-ing a dangerous limit. If the dangerous warning is given before the limit reaching, the security measures could be taken ahead of the scene, it is possible to avoid the accident. Application of neural network theory, and based on improving of learning algorithm, build a BP model of predicting Monitoring data, and carry out the simulation by Matlab. Through the test of Monitoring data, confirmed the validity of the improved algorithm and the model.更多还原