|
|||||
|
|
| 基于遗传算法改进BP网络的地表沉陷预计 | |
| 引用本文： | 肖波,麻凤海,杨帆,张荣亮.基于遗传算法改进BP网络的地表沉陷预计[J].中国矿业,2005,14(10):83-86. |
| 作者姓名： | 肖波 麻凤海 杨帆 张荣亮 |
| 作者单位： | 1. 辽宁工程技术大学土木建筑工程学院,阜新,123000 2. 辽宁工程技术大学学科建设办公室,阜新,123000 3. 辽宁工程技术大学测量工程系,阜新,123000 |
| 基金项目： | 辽宁省教育厅基金（202183392）,辽宁省自然科学基金（20022158）和辽宁省中青年学科带头人培养基金联合资助. |
| 摘 要： | 本文采用遗传学习算法和误差反向传播算法相结合的混合算法来训练前馈人工神经网络，即先用遗传学习算法进行全局训练，再用BP算法进行精确训练，使网络收敛速度加快和避免局部极小。作为实例，将该方法应用于地表沉陷预计问题中。建立了采动地表沉陷的神经网络预计模型，利用矿区大量的地表沉陷实际观测数据样本对该神经网络进行训练和学习，并用该网络对几组数据进行采动地表沉陷预计。结果表明，该神经网络预计模型具有收敛速度快、预测精度高的优点，为采动地表沉陷预计提供了实用的方法。 |
| 关 键 词： | 人工神经网络 遗传学习算法 BP算法 采动地表沉隐预测 |
| 文章编号： | 1004-4051（2005）10-0083-04 |
| 收稿时间： | 2005-06-22 |
| 修稿时间： | 2005年6月22日 |
SURFACE SUBSIDENCE PREDICTION IN MINED-OUT BASED ON GENETIC-ALGORITHM-OPTIMIZED BP NETWORK |
|
| Xiao Bo,Ma Fenghai,Yang Fan,Zhang Rongliang.SURFACE SUBSIDENCE PREDICTION IN MINED-OUT BASED ON GENETIC-ALGORITHM-OPTIMIZED BP NETWORK[J].China Mining Magazine,2005,14(10):83-86. | |
| Authors: | Xiao Bo Ma Fenghai Yang Fan Zhang Rongliang |
| Affiliation: | 1. School of Civil Architecture Engineering, Liaoning Technical University , Fuxin 123000; 2. Office of Discipline Construction, Liaoning Technical University , Fuxin 123000; 3. Department of Surveying Engineering, Liaoning Technical University , Fuxin 123000 |
| Abstract: | A new method for training the artificial neural network is presented.In this method,the genetic algorithm(GA),a general-purpose global search algorithm is used to train the network with updating the weights to minimize the error between the network output and the desired output.Then the back-propagation(BP)algorithm is used to further train the artificial neural network.The method is used to speed up the convergence and improve the performance.As an example,the method was used to predict surface subsidence in mined-out.An neural network prediction model was established.A lot of practical surveying data sets are collected to train the neural network.Several sets are used to predict the surface subsidence by the neural network.Results show that the neural network prediction model is of high convergent speed and good prediction precision,so the model offers a useful approach for surface subsidence prediction in mined-out. |
| Keywords: | Artificial neural network Genetic algorithm Back-propagation algorithm Surface subsidence prediction in mined-out |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |