广义LVQ神经网络的性能分析及其改进

BEHAVIORAL ANALYSIS AND IMPROVING OF GENERALIZED LVQ NEURAL NETWORK

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．

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.