摘要
从简要归纳与分析现有的神经网络模型的角度出发,讨论了GMDH网络模型的结构、特点及其输入输出关系.提出了一种基于GMDH模型的神经网络学习算法,详细阐述了该算法的主要设计思想与实现过程,并就算法停止准则和网络最佳层数问题进行了仿真研究.实践表明,该算法自组织性强,表现出了较好的泛化能力和稳定性.
From the perspective of summarizing and analyzing the existing neural networks, it is discussed the structure, the character and the input/output relationship of the GMDH neural network model. Then,it is introduced a sort of neural network learning algorithm based on the GMDH model, described the main design idea and the realization process of this algorithm, and used simulated technique to research the problem of how to make the algorithm stop and how to find the optimal number for network layers. Through experiments, it is shown that the algorithm has strong self-organization ability, preferable generalization ability and stability.
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2008年第6期569-574,共6页
Journal of Yunnan University(Natural Sciences Edition)
基金
肇庆市科技计划资助项目(2007G013)
关键词
神经网络
学习算法
GMDH
自组织
neural network
learning algorithm
Group Method of Data Handling
self-organization
