摘要
采用基于递推预报误差算法的分布式神经网络结构建立非线性系统模型 .子神经网络模型及其连接权值均采用递推预报误差方法来进行训练 ,将所有子网络融合得到的分布式神经网络模型在模型精确性和鲁棒性方面有显著地增加 .
Improved predictions can be obtained by using distributed neural networks (DNNs) instead of an individual better network as usual. New approach to obtain combination weights of multiple networks is derived based on parallel recursive prediction error algorithm. Model accuracy and robustness can be significantly improved by using distributed neural networks. The proposed method has been applied and evaluated for a complex dynamic nonlinear system. Results obtained demonstrate that this approach can improve the performance of neural network based nonlinear models.
出处
《信息与控制》
CSCD
北大核心
2000年第5期414-420,共7页
Information and Control
关键词
分布式神经网络
递推预报误差算法
非线性系统
distributed neural network, parallel recursive prediction error algorithm, nonlinear system
