摘要
针对神经网络建模效果对网络结构、训练方法过于敏感的缺陷,提出一种结构自适应神经网络模型及其训练方法.模型具有双网结构并以"提前终止法"训练,一定程度上降低了建模效果对网络结构的敏感性;模型结构根据建模数据的噪声方差、模型当前误差等信息自适应调整,进一步提高了模型的建模效果,同时具有较高的时间效率.仿真结果表明,该方法弥补了提前终止等传统方法的部分不足,具有较好的效果.
The performance of artificial neural network（ANN） is very sensitive to its structure and training method.Therefore,an adaptive structure ANN and its training method are proposed.The ANN model is with double-net structure and trained by early stopping,which reduces the sensitivity of model performance to structure.The ANN structure is proposed to be adjusted dynamically according to the information of data noise and model error,which further improves the model performance with relatively high time efficiency.Simulation results show that the proposed methods can make up some defects of traditional methods such as early stopping,and has better performance.
出处
《控制与决策》
EI
CSCD
北大核心
2010年第8期1265-1268,共4页
Control and Decision
基金
国家自然科学基金重点项目(60634020)
湖南省教育厅科技项目(08W003)
关键词
优化建模
训练策略
神经网络
结构自适应
Optimal modeling
Training strategy
Neural network
Structure adapting
