中文摘要：

针对平面高斯神经（Plane-Gaussian,PG）网络采用k-平面聚类算法得到网络参数,使得网络训练时间过长,且易陷入局部极小值的问题,借鉴极限学习机（Extreme learning machine,ELM）中网络参数随机选择的方式,提出了随机投影下的平面高斯神经网络（Plane-Gaussian network based on random projection,RandPG）。该网络采用随机投影的方式确定隐层激活函数的参数,然后利用Moore-Penrose广义逆求解输出层权值。理论上证明该网络具有全局逼近性。同时,对呈直线型和平面型的人工数据集以及UCI标准数据库中的分类数据集进行测试,结果表明,RandPG网络提供了一种简便的参数学习方法,并且在继承了PG网络对呈子空间分布的数据分类具有优势的情况下,显著提高了网络的学习速度。

英文摘要：

For the Plane-Gaussian（PG）artificial network,its network parameters are generated from kplane clustering algorithm in training phase.Compared with random parameters of extreme learning machine（ELM）,PG is a time-consumer and easy to trap into local optimal solution.To improve the performance of PG network,inspired by ELM in this paper,a new training method based on random projection for PG network,termed as RandPG,is proposed.Typically,for the three-layer network,the weight matrix between input and hidden layers is selected by random projection to speed training network,and the weight matrix between hidden and output layers is obtained by Moore-Penrose generalized inverse.It is proved that the network has global approximation theoretically.Meanwhile,the effectiveness of this network is tested on the line-distribute datasets,plane-distribute datasets and several UCI datasets.The results indicate that RandPG provides a simple and convenient way to train parameters of neural network,and it not only follows the advantage of PG network,which is more suitable for classifying subspace-distribute datasets,but also significantly accelerates its learning speed.