摘要
本文提出了非线性对象神经网络建模的广义自组织学习算法,该算法采用多个局部模型进行建模,扩展了Kohonen自组织学习算法中的局部模型划分机制,且多个局部模型的划分兼顾了输入样本的分布和模型匹配特性.仿真结果表明,广义自组织学习算法明显地提高了建模精度和收敛速度.
A neural network modelling method for nonlinear plants by using a new learning algorithm, Generalized Self-organized Learning (GSL), is proposed in this paper. This method employs multiple local models for plant modelling. It develops the division mechanism. for local models adopted in the Kohonen self-organized learning algorithm, and the achieved local models take both the distribution of input samples and the model matching errors into account. Simulation results show that the GSL algorithm improves modelling accuracy and learning speed obviously.
出处
《电子学报》
EI
CAS
CSCD
北大核心
1992年第10期56-60,共5页
Acta Electronica Sinica
基金
国家自然科学基金
关键词
神经网络
非线性对象
建模
Neural network, Nonlinear plant modelling, Self-organized learning, Back propagation learning, Pattren clustering
