中国科学院半导体研究所神经网络组,北京,100083
纸质出版:2004
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曹 宇, 赵星涛. 一种新型双权值人工神经元网络的数据拟合研究[J]. 电子学报, 2004,32(10):1671-1673.
CAO Yu, ZHAO Xing-tao. Data Fitting Based on a New Double Weights Neural Network[J]. Acta Electronica Sinica, 2004, 32(10): 1671-1673.
在本文中提出了一种针对新型双权值神经元网络的数据拟合算法.采用这种新型网络结构和算法
可以克服传统的通用前馈网络中BP算法易陷入局部极小的问题.通过实验比较证明在相同的网络规模下
采用这种新型网络结构和算法可以取得比径向基(RBF)网络更高的拟合精度和更少的迭代次数.
We construct a new method in data fitting fields.Usually
in traditional BP neural network model
data fitting may become trapped at a local minimum.By using the new Double Weights Model
this algorithm can give the Direction Weight
also the Central Weight at the same time.Experimental results show that this algorithm can get more accurate fitting effects and use less generations to calculate
compared with the RBF (Radial Basis Functions) while using the same environment and equal network scale.Data fitting on it should be a new method to modern industry applications in data control and analyses and so on.
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