基于模糊神经网络的Horn集上的输入归结  

Input Resolution on Horn Sets Based on Fuzzy Neural Networks

作　　者：裴峥[1] 黄天民[1] 

机构地区：[1]西南交通大学应用数学系,四川成都610031

出　　处：《西南交通大学学报》2002年第5期565-569,共5页Journal of Southwest Jiaotong University

基　　金：国家自然科学基金 (6 0 0 74 0 14 )

摘　　要：关于定理机器证明的归结原理已应用到人工智能的很多领域 ,同时提出了各种改进方法。其中 ,输入归结是一种非常好的推理方法 ,它对于Horn集是完备的。模糊神经网络是模糊逻辑与神经网络的融合 ,文中利用模糊神经网络的知识表示及学习的特点 ,结合输入归结的优点 ,进行Horn集上的输入归结。Resolution principle of automated reasoning has been used in many aspects of artificial intelligence, and many modified methods have been proposed. In the modified methods, input resolution is one of good reasoning methods, and it is complete for Horn sets. Fuzzy neural network is a combination of fuzzy logic and neural networks. In this paper, by using the character of knowledge representation and learning of fuzzy neural networks, input resolution on Horn sets is implemented.

关 键 词：模糊神经网络 HORN集 模糊逻辑系统 归结原理 输入归结 人工智能 机器证明 

分 类 号：O159[理学—数学] TP183[理学—基础数学]