摘要
由于煤与瓦斯突出是一典型的复杂非线性动力系统,影响因素很多,如应力、煤层特征、构造等,且各影响因素相互关联,因此,采用非线性人工神经网络进行煤与瓦斯突出的模式辨识与预测是十分必要的。针对具体的不同煤层条件,建立自适应小波基神经网络激活函数模型,用于煤与瓦斯突出系统的辨识和预测,实现由网络本身自动确定神经单元的数目,避免人为因素的影响,提高辨识和预测的可靠性和智能化程度。实例分析结果表明,所建立的自适应小波基神经网络激活函数模型,辨识和预测精度高,具有重要的推广应用价值。
As matter of fact,the evolution system of coal and gas outbursts is a typical nonlinear dynamical one;there are many factors such as stress,thickness variation of seam,geological fault,etc.influencing it,and these factors are correlative.It is necessary to build a nonlinear artificial neural network（ANN） to recognize the pattern of coal and gas outbursts and to predicate coal and gas outbursts intensity.A self-adaptive wavelet neural network for recognizing pattern of coal and gas outbursts and for predicating coal and gas outbursts intensity has been built by considering different coal seam and gas conditions,which can generate the neural element numbers automatically and can avoid the jamming for determining the element number in BP network artificially.This ensures the reliability and intelligence of recognition and predication.It is verified by some examples that the model has a high accuracy for recognition and predication;and it is valuable for generalizations and applications.
出处
《岩石力学与工程学报》
EI
CAS
CSCD
北大核心
2007年第A01期3373-3377,共5页
Chinese Journal of Rock Mechanics and Engineering
基金
国家自然科学基金重点项目(50534080)
国家自然科学基金资助项目(50674063)
山东省自然科学基金资助项目(Y2004F11)
山东省教育厅计划项目(J06N04)
山东科技大学矿山灾害预防控制教育部重点实验室开放基金
山东省"泰山学者"建设工程专项经费联合资助项目
关键词
采矿工程
煤与瓦斯突出
自适应小波基
神经网络
辨识
预测
mining engineering
coal and gas outbursts
self-adaptive wavelet base
artificial neural network（ANN）
recognition
predication
