摘要
分析了满足给定学习误差要求的最小结构神经网络的各种实现方法.把粗糙集理论引入神经网络的结构构造中;提出了一种基于粗糙集理论的RBF神经网络剪枝算法,并将这种算法与现有剪枝算法相比较.最后将该算法应用于热工过程中过热气温动态特性建模.仿真结果表明基于该算法的神经网络模型具有较高的建模精度以及泛化能力.
Methods to achieve the smallest sized network which can learn the training data within a given error bound are analyzed.Rough sets theory is applied to construct neural networks.A pruning algorithm for RBF（radial basis function） neural network based on rough sets is proposed,and this algorithm is compared with the existing methods.The proposed algorithm is applied to build the dynamic model of the superheated steam temperature in thermal process.Simulation is made,and the results show that the neural model based on this algorithm is of high approximation accuracy and good generalization ability.
出处
《信息与控制》
CSCD
北大核心
2007年第5期604-609,615,共7页
Information and Control
关键词
粗糙集
剪枝
RBF
rough set
pruning
RBF（radial basis function）
