【摘要】：隨著礦井環(huán)境信息感知、危險源辨識等技術的發(fā)展,對氣體傳感器檢測精度和可靠性的要求顯著提高。為改善礦用氣體傳感器的性能,針對氣體傳感器補償方法存在的技術難題,提出一種微粒群優(yōu)化支持向量機(PSO-SVM)的非線性補償方法。以CO傳感器為例,采用Matlab軟件進行數(shù)值仿真,BP神經網絡方法將誤差從18.48%降到8.51%,而采用微粒群優(yōu)化支持向量機方法將誤差降到5.28%。實驗結果表明:PSO-SVM補償方法能有效消除非目標參量對傳感器輸出結果的影響從而完成非線性補償,提高了礦用CO傳感器的可靠性與檢測精度。
[Abstract]:With the development of mine environmental information perception and hazard source identification, the detection accuracy and reliability of gas sensors are greatly improved. In order to improve the performance of mine gas sensor, a nonlinear compensation method based on particle swarm optimization support vector machine (PSO-SVM) is proposed to solve the technical problems of gas sensor compensation. Taking CO sensor as an example, the error is reduced from 18.48% to 8.51% by using Matlab software and the error is reduced to 5.28% by using particle swarm optimization support vector machine method. The experimental results show that the proportion PSO-SVM compensation method can effectively eliminate the influence of non-target parameters on the output results of the sensor and thus achieve nonlinear compensation. The reliability and detection accuracy of the mine CO sensor are improved.
【作者單位】： 西安科技大學安全科學與工程學院;陜西省煤火災害防治重點實驗室;西安科技大學電氣與控制工程學院;
【基金】：國家自然科學基金項目(51504186) 陜西省科技攻關項目(2016GY-191) 省教育廳科研專項項目(14JK1477)
【分類號】：TD711;TP18;TP212
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本文編號：2194143