基于优化的粗神经网络在地质样品元素的预测研究  

ON APPLYING OPTIMISATION-BASED ROUGH NEURAL NETWORK IN PREDICTING ELEMENTS OF GEOLOGICAL SAMPLES

作　　者：黄胜忠[1] 罗芳琼[1] 陈旭明 

机构地区：[1]柳州师范高等专科学校数学与计算机科学系,广西柳州545004 [2]广西地球物理勘查院,广西柳州545112

出　　处：《计算机应用与软件》2014年第5期159-162,共4页Computer Applications and Software

基　　金：广西教育科研基金项目(201204LX506);新世纪广西高等教改科研工程项目(2010JGB135)

摘　　要：在神经网络中引入粗糙集理论和模糊聚类方法,实现建模预测。首先用粗糙集和模糊聚类进行属性约简,去掉冗余的属性。然后根据模糊逻辑规则获取合理的网络输入层、隐含层和输出层,建立优化的粗神经网络预测模型。该模型可以有效地去除神经网络中输入层的冗余神经元,合理地确定隐含层神经元的数目,使神经网络提高收敛性能,获得更好的非线性逼近能力。仿真实验结果说明:优化的粗神经网络预测模型,可提取有用信息,简化网络结构,减少训练时间,提高预测精度。在地质样品元素的预测实验中,取得了良好的效果。We incorporate rough set theory and fuzzy clustering method into neural network to realise modelling and forecasting. First we use rough sets and fuzzy clustering to conduct attribute reduction,and remove redundant attributes. Then according to the rules of fuzzy logic we obtain reasonable network input layer,hidden layer and output layer,and build the optimised rough neural network prediction model. The model can effectively remove redundant neurons in input layer of the neural network and reasonably determine the number of neurons in hidden layer,the neural network gets the improved convergence performance and better nonlinear approximation ability. Simulation experimental result demonstrates that the optimised rough neural network prediction model is able to extract useful information and simplify the network structure,it can reduce the training time and improve the prediction accuracy as well. In prediction experiment on elements of geological samples,it achieves good effects.

关 键 词：粗糙集 模糊聚类 神经网络 样品元素 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]