基于ANN的综放工作面产量与工效预测

采煤工作面是整个矿井生产的核心，是实现矿井高产高效安全生产的关键。为了准确预测和提高综放工作面产量与工效，本文应用人工智能领域的人工神经网络方法，在系统分析影响综放工作面产量、工效的主要因素，并收集大量工程实例样本的基础上，构建了综放工作面产量与工效预测人工神经网络模型并加以实际应用。应用结果表明，该方法简便、可靠，且具有先进性。该方法的成功应用，为煤炭产量、工效预测研究探索了一条更加先进有效的途径。

Prediction research on output and work efficiency of fully mechanized sub-level caving face based on artificial neural network

Working face is the core for production of coal pits, and is the key to high output and work efficiency of coal pits. In the paper, for the sake of exact prediction and raising of output and work efficiency in fully mechanized sub-level caving face, based on systemic analysis of primary influencing factors of output and work efficiency in fully mechanized sub-level caving face, and also based on large numbers of collected case history samples, artificial neural network method which belongs to artificial intelligence field was used, and artificial neural network model for output and work efficiency prediction in fully mechanized sub-level caving face was constructed and applied. Application results show that this technique has the characteristics such as simplicity, credibility and superiority. The successful application of this technique has explored one more superior and effective path for prediction research on coal output and work efficiency.