摘要
研究粗糙近似算子关于模式“二分法”的相关性质,描述了利用给定模式把模式空间划分成两组的模式 分类的可能性和必然性,并设计了有边界区域的模式分类的可能性和必然性的粗糙神经网络算法.最后,用仿真实验 验证了算法的有效性.
The properties of rough approximate operator for bisection method of pattern are discussed. The possibility and necessity of pattern classification by dividing pattern space into two decision areas using the given patterns are described. The rough neural networks algorithm of the possibility and necessity of the classification problem with boundary areas is designed. Finally, numerical examples are given to illustrate the efficiency of algorithm.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2004年第4期697-700,共4页
Acta Electronica Sinica
基金
陕西省自然科学基金(No.2001SL038)
关键词
粗糙近似算子
模式分类
可能性和必然性
Computer simulation
Data processing
Learning algorithms
Neural networks
Probability
Rough set theory
