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矿井瓦斯涌出量受诸多因素影响,对瓦斯涌出量的预测研究是一项复杂且难度较高的工作。针对其复杂性及随机性,笔者提出了基于粒子群优化相关向量机的矿井瓦斯涌出量的预测控制方法。并利用相关向量机对矿井瓦斯涌出非线性系统进行建模,采用具有全局优化特点的粒子群优化算法进行参数优化,确保了模型精度和预测的准确性。结果表明,该模型预测控制方法精度高且可靠性强,预测效果理想。在矿井瓦斯涌出量的预测研究应用中,误差在工程许的范围内,对矿井的安全生产具有一定的参考意义。
Gas emission from mines is affected by many factors. Prediction of gas emission is a complicated and difficult task. In view of its complexity and randomness, the author proposes a predictive control method of mine gas emission based on PSO-SVC. The gas emission nonlinear system of the mine was modeled by using the correlation vector machine. The particle swarm optimization algorithm with global optimization was used to optimize the parameters, which ensured the model accuracy and prediction accuracy. The results show that the model predictive control method with high accuracy and reliability, the prediction effect is satisfactory. In the prediction of mine gas emission research and application, the error in the scope of engineering permits, the mine safety of production has some reference value.