%0 Journal Article
%T Soft Competitive Algorithms for Learning Vector Quantization<br>学习矢量量化的软竞争算法
%A ZHANG Zhi-hu
%A ZHENG Nan-ning
%A WANG Tian-shu
%A <br>张志华
%A 郑南宁
%A 王天树
%J 软件学报
%D 2002
%I 
%X Though the loss factors in FALVQ algorithms are defined to fuzzy membership functions, the performance of the algorithms is not stable due to their scaling functions not being fuzzy membership functions. In this paper, a new family of fuzzy algorithms for the generalized LVQ network, called soft competitive algorithms for LVQ (SCALVQ), is derived from extending the definition of the loss factor corresponding to the winning prototype in FALVQ. Meanwhile, three concrete types of SCALVQ are given. In SCALVQ, the loss factors and the corresponding scaling function are both fuzzy membership functions,but an identical fuzzy membership function.Therefore,they absorb advantages of FALVQ and the soft-competition scheme,and overcome the disadvantages of the FALVQ.
%K fuzzy membership function
%K loss factor
%K scaling function
%K interference function<br>模糊隶属度函数
%K 亏损因子
%K 尺度函数
%K 干扰函数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=40D22457F3641216&yid=C3ACC247184A22C1&vid=FC0714F8D2EB605D&iid=94C357A881DFC066&sid=724110922AF7E025&eid=4CA738ADDC4F9A9D&journal_id=1000-9825&journal_name=软件学报&referenced_num=1&reference_num=11