首页> 外文期刊>Neural Computing & Applications >Neural network training with optimal bounded ellipsoid algorithm
【24h】

Neural network training with optimal bounded ellipsoid algorithm

机译:最优有界椭球算法的神经网络训练

获取原文
获取原文并翻译 | 示例

摘要

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied in training the weights of the feedforward neural network for nonlinear system identification. Both hidden layers and output layers can be updated. From a dynamic system point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. Two simulations give the effectiveness of the suggested algorithm.
机译:与常规学习算法(例如,反向传播)相比,最优有界椭球(OBE)算法具有一些更好的属性,例如更快的收敛性,因为它的结构与Kalman滤波器相似。 OBE与卡尔曼滤波器训练相比具有一些优势,不需要噪声是高斯噪声。本文将OBE算法应用于训练前馈神经网络的权重以进行非线性系统辨识。隐藏层和输出层都可以更新。从动态系统的角度来看,这种训练对于需要实时更新权重的所有神经网络应用都是有用的。两次仿真给出了所建议算法的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号