摘要: 将粗糙集与神经网络结合,提出由样本更新、粗糙集预处理、神经网络训练、规则提取4个模块组成的煤矿瓦斯预测知识获取模型,将其应用于实时数据进行实验,结果表明,该模型实时性好、可靠性及精度高,可以较好地解决煤矿瓦斯预测知识获取困难的问题,为煤矿瓦斯预测专家系统知识库的建立奠定基础。
关键词:
知识获取,
煤矿瓦斯预测,
粗糙集,
神经网络
Abstract:
By combining rough sets and neural networks, this paper develops a knowledge acquisition model for colliery gas forecast, which consists of four modules: sample refreshment, rough sets preprocessing, neural network training and rules extraction. It is applied in the real-time data, whose results show that it solves the problem of the knowledge acquisition difficulty of colliery gas forecast, and has good real-time characteristic, high reliability and perfect precision. The model provides foundation for establishing knowledge database of colliery gas forecast.
Key words:
knowledge acquisition,
colliery gas forecast,
rough sets,
neural networks
中图分类号:
Application Research of Knowledge Acquisition Model for Colliery Gas Forecast. 煤矿瓦斯预测知识获取模型的应用研究[J]. 计算机工程, 2009, 35(12): 169-171.
孙林嘉;李 茹;屈元子. Application Research of Knowledge Acquisition Model for Colliery Gas Forecast[J]. Computer Engineering, 2009, 35(12): 169-171.