%0 Journal Article
%T Efficient learning algorithm for Fuzzy bi-directional associative memory based on Lukasiewicz''''s t-Norm<br>基于Lukasiewicz t-模的模糊双向联想记忆网络的有效学习算法
%A ZENG Shui-ling
%A XU Wei-hong
%A <br>曾水玲
%A 徐蔚鸿
%J 计算机应用
%D 2006
%I 
%X Taking advantage of the concomitant implication operator of T_ L , which is a t-norm, a simple efficient learning algorithm was proposed for the fuzzy bi-directional associative memory based on fuzzy composition of Max and T_ L (Max-T_ L FBAM). It is proved theoretically that, if there is a connected weight matrix which make arbitrarily given pattern pairs set become stability state set of Max-T_ L FBAM, then the proposed learning algorithm can find the maximum of all connected weight matrices . An experiment was given to test the effectiveness of the presented learning algorithm.
%K concomitant implication operator
%K Fuzzy bi-directional associative memory
%K learning algorithm
%K t-norm<br>伴随蕴涵算子
%K 模糊双向联想记忆网络
%K 学习算法
%K t-模
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=BE7509EF9D943859&yid=37904DC365DD7266&vid=96C778EE049EE47D&iid=59906B3B2830C2C5&sid=828422472198C4ED&eid=29AE32B30980BD5E&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=13