Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material

Qing-liang CHANG, Hua-qiang ZHOU, Chao-jiong HOU
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引用次数: 17

Abstract

In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural networks. Based on cases related to our test data of filling material, the predicted results of the model and measured values are compared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.

利用人工神经网络中的粒子群优化算法对膏体填充材料的强度进行预测
为了准确预测充填材料的强度,分析了影响充填材料强度的主要因素。应用人工神经网络理论,建立了充填材料强度预测模型。结合笔者充填材料试验数据的相关实例,对模型的预测结果与实测值进行了对比分析。结果表明,该模型预测充填体强度是可行的、科学合理的,为煤矿膏体充填体强度预测提供了一种新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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