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针对传统信息融合技术在煤矿井下环境等级评价中的局限性,文章提出了一种智能算法:通过粒子群优化算法对最小二乘支持向量机进行参数寻优,建立多传感器信息融合模型PSO-LSSVM,克服参数选择的主观性、盲目性,从而提高算法的分类精度和收敛速度。实验结果表明,相比未经参数优化的最小二乘支持向量机模型、网格算法优化最小二乘支持向量机模型,PSO-LSSVM模型能很好地解决煤矿井下环境等级评价中小样本的高维、非线性、不确定性等方面的问题。
Aiming at the limitation of traditional information fusion technology in coal mine environment level evaluation, an intelligent algorithm is proposed in this paper: Particle swarm optimization algorithm is used to optimize the parameters of least squares support vector machines, and the multi-sensor information fusion model PSO-LSSVM , To overcome the subjectivity and blindness of parameter selection, so as to improve the classification accuracy and convergence speed of the algorithm. The experimental results show that compared with the least-squares support vector machine (LS-SVM) model without optimization, the grid algorithm optimizes the LS-SVM model. The PSO-LSSVM model can well solve the high-dimensional , Non-linear, uncertainties and other issues.