摘要
在模糊artmap网络中,Fa2层每增加一个的神经元就表示增加了一个聚类,这是该网络的优点同时也增加了网络的负荷。文中对模糊artmap算法进行了简要介绍并分析了算法的收敛复杂度。在此基础上,提出数据集划分法以提高网络的收敛速度。最后通过试验比较算法改进前后的网络收敛速度的差异,证明改进后网络的收敛速度明显提高。
One of the properties of fuzzy artmap, which can be both an asset and a liability, is its capacity to produce new neurons （templates） on demand to represent classification categories. This property allows fuzzy artmap to automatically adapt to the database without having to arbitrarily specify network structure. Provide one method for speeding up the fuzzy artmap algorithm, the method referred to as the data partitioning partitions the data into subsets for independent processing. Provide experimental results on a Beowulf cluster of workstations for the approach that confirms the merit of the modifications.
出处
《计算机技术与发展》
2006年第11期16-18,共3页
Computer Technology and Development
基金
北京市自然科学基金资助项目(4032009)
