基于RBFNN的薄煤层采煤机截割性能的评价

Cutting performance evaluation for thin seam shearer based on RBFNN

采煤机由于其高产高效,适应性强,被广泛地应用于各种地质条件的煤矿开采,因此十分有必要对采煤机的可靠性进行有效地分析.通过建立径向基神经网络,利用虚拟样机技术提取的样本数据,结合模糊数学对采煤机的截割性能进行评价分级并进行评价预测,预测的结果和实际评价效果十分接近.结果表明该模型有较高的准确性,具有一定的应用价值.

The shearer is widely used for coal mining under different geological conditions due to its high production,high efficiency and better suitability.It is necessary to effectively analyze the reliability of the shearer.Established RBF neural network in this paper,extracted the sample data by virtual prototype technology,combined with fuzzy mathematics to research evaluation,classify and predict the shearer cutting performance,the prediction results are very close to the actual evaluation. The results show that evaluation model has high accuracy and certain application value.