粗糙集约简的WNN隐层节点优化方法  

Optimization method for hidden layer nodes of WNN based on rough set reduction

作　　者：孟月波[1,2] 邹建华[1] 刘光辉[2] 甘旭升[3] 

机构地区：[1]西安交通大学系统工程研究所,西安710049 [2]西安建筑科技大学信息与控制工程学院,西安710055 [3]空军工程大学工程学院,西安710038

出　　处：《控制与决策》2014年第6期1091-1096,共6页Control and Decision

基　　金：陕西省自然科学基金项目(2013JM8030;2012JM8026);陕西省教育厅专项基金项目(2013JK1091)

摘　　要：在确保网络性能的前提下,如何确定最佳隐层节点,获得最简网络结构是小波神经网络(WNN)应用推广的关键.对此,引入粗糙集理论,提出了基于信息熵的卡方离散化算法和启发式的属性约简递归算法,利用粗糙集约简过程对WNN隐层节点进行精简,并将其应用于飞行器气动力建模.仿真结果表明,采用改进的粗糙集方法设计WNN,不仅能够简化网络结构,而且与未经结构优化的WNN相比,其模型精度和训练速度都得到了实质性改善.Under the premise of ensuring network performance, the key of wavelet neural network（WNN） application and promotion is how to get the most simple network structure by determining the optimal hidden layer nodes. Therefore, a Chi-Square discretization algorithm based on the information entropy and heuristic attribute reduction recursive algorithm is proposed, reduction process of the rough set theory is used to optimize wavelet neural network hidden layer nodes without changing network performance, and an aircraft aerodynamic model is built by modifying wavelet neural network. Simulation results show that WNN optimized by the proposed improved rough set method the can not only simplify the network structure, but also improve model accuracy and training speed.

关 键 词：小波神经网络 粗糙集 气动力建模 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]