摘要
Wang-Mendel算法是生成模糊规则库的经典算法.处理过程中,当样本数据存在噪声时,该算法易提取出可信度较低的规则;当样本数据规模增大时,算法效率易快速下降.针对这两个问题,引入样本间协调关系可提高结果的准确性,改善逼近性能.利用SOM算法对样本预处理可有效去噪,且其对样本分布的自适应学习能力可在一定程度上减小样本规模.基于样本相关度和SOM算法,文中提出一种Wang-Mendel模糊规则提取算法,函数逼近和列车控制系统的仿真实验结果表明其具有较好的完备性、鲁棒性和效率.
Wang-Mendel algorithm is commonly used as a classic method to generate fuzzy rule base. But rules with low confidence are usually extracted when noise appears in the sample data set, while its efficiency also often drops fast when the scale of sample data increases. To solve those problems, two methods, cooperation relationship and self-organizing mapping （SOM） neural network, are introduced. Cooperation relationship among sample data improves the accuracy of rules and approximation ability to the original model. On the other hand, SOM can well preprocess sample data for denoising and reduce its scale through a self-adaptive learning procedure of weights network. Then an improved Wang-Mendel algorithm is proposed based on cooperation relationship degree of sample data and SOM. The experimental results, including trigonometric function approximation and artificial driving simulation of a train operation control system, show its completeness, robustness and operating efficiency.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第11期1079-1088,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61103170)
福建省教育厅A类科技项目(No.JA12005)资助
关键词
模糊规则
完备性
鲁棒性
Fuzzy Rules, Completeness, Robustness
作者简介
缑锦（通讯作者），男，1978年生，博士，副教授，主要研究方向为人工智能、知识工程．E-mail：goujin@hqu．edu.cn．
陈文瑜，男，1987年生，硕士研究生，主要研究方向为模糊系统．
