欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
分析了模糊K-近邻方法FKNN，研究了模糊最小最大神经网络FMMNN分类方法，然后深入分析了支持向量机SVM原理，并在此基础上给出了一种改进的径向基核函数。基于IRIS数据，进行了计算机仿真实验。结果表明，改进的SVM方法分类性能比模糊最小最大神经网络与模糊K-近邻算法的分类性能更好，且运算时间更短，更易于实时实现。
英文摘要：
Pattern classification is not only an important aspect in pattern recognition but also the key technique in transaction of other questions. Firstly the method of FKNN is analyzed. Secondly the method of FMMNN is also studied. Thirdly studying the theory of SVM, this paper presents an ameliorated RBF kernel function. Finally, the simulation is processed based on the IRIS data and the results are compared with each other. These results show that the performance of the ameliorated SVM outperforms those of the FMMNN and FKNN, with shorter operation time and more suitable for real-time implementation.
同期刊论文项目
同项目期刊论文
期刊信息
位置：