摘要
传统中由单一的神经网络等算法所构架起的评价模型主要存在着精度低、网络学习速度慢等不合理之处。为此,提出了基于粗糙集和RBF神经网络的大规模数据集环境下的评价方法。首先详解了粗糙集理论对大规模高维数据所确定的宽泛属性集的分类、约简;然后把处理后的数据指标作为RBF神经网络的输入进行训练、仿真。以高速公路路面性能使用评价为例,给出了该方法的具体实现过程,与没有采用属性约简的RBF神经网络进行结果对比,该方法在大规模高维数据集应用评价上更具有实用性、有效性。
The evaluation model is constructed by single neural network has many faults with the large high-dimensional data sets, such as low accuracy, slow speed and so on. Therefore, a new evaluation way based on rough set ＆ RBF neural network with the large high-dimensional data sets is proposed. Firstly, it uses rough set in classing and simplifying the broad attribute sets which is determined by large high-dimensional data sets; Then it establishes the RBF neural network model to deal with the index which is simplified. Finally, through the example, it gives the method to realize the process specifically. Compared with the RBF neural network which does not use the reductive index,this method has better practicability and validity in pavement performance evaluation.
出处
《自动化技术与应用》
2011年第9期1-5,共5页
Techniques of Automation and Applications
基金
湖北省高教科研项目(编号2009361)
