摘要
文中提出了一种基于免疫原理的新型径向基函数(RBF—Radial Basis Function)神经网络模型。该模型利用人工免疫系统的记忆、学习和自组织调节原理,进行RBF神经网络隐层中心数量和位置的选择,并采用递推最小二乘算法确定网络输出层的权值。将这种新型的RBF神经网络应用于建立热工过程的非线性模型。仿真研究表明,这种建模方法不仅计算量较小,而且精度高,并有较好的泛化能力。
A novel RBF neural network model based on immune principles is presented in this paper. The memory, learning and self-organization abilities of artificial immune system are introduced to the selecting of the number and positions of hidden layer RBF centers, the output layer weights are decided with the recursive least squares algorithm. The nonlinear model of thermal processes is estabilished with the novel RBF neural network, simulation study proves that the method has less calculation, high precision and good generalization ability.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2002年第9期118-122,共5页
Proceedings of the CSEE
基金
国家自然科学基金项目(50076008)
江苏省青年科技基金项目(BQ2000002)。~~
关键词
锅炉
过热器
RBF神经网络
热工过程
建模
radial basis function
neural network
artificial immune system
hybrid learning algorithm
thermal processes
modelling
