摘要:
首先从理论上分析了广义学习矢量量化(GLVQ)网络的GLVQ-F算法的性能,
GLVQ-F算法在一定程度上克服了GLVQ算法存在的问题.然而,它对获胜表现型的学习具
有好的性能,对于其它的表现型,性能却十分不稳定,分析了产生这个问题的原因,直接从表
现型的学习率出发,提出了选取学习率的准则,并给出了两种改进的算法.最后,使用IRIS数
据验证了算法的性能,改进算法较之GLVQ-F算法具有明显的稳定性和有效性.
Abstract:
In this paper, the performance of GLVQ-F algorithm of GLVQ network
is theoretically analyzed. The GLVQ-F algorithm, to some extent, has overcome
the shortcomings that GLVQ algorithm possesses. But, there are some problems
in GLVQ-F algorithm, for example, the algorithm has good performance on
the winning prototype, and on other prototypes, its performance is very unstable.
In this paper, the reasons of the problem are discussed. The rules of choosing the
learning rates are proposed, and two modified algorithms are developed therefrom.
Finally, the performance of the modified algorithms is verified with IRIS data,
which shows the modified algorithms are more stable and effective than GLVQ-F algorithm.
点击查看大图
下载:
