基于岛屿迁徙模型的RBF网络集成及其应用研究

神经网络集成是一种通过组合每个神经网络的输出生成最后预测的很流行的学习方法,可以显著地提高学习系统的泛化能力。为了提高集成方法的有效性,提出了一种基于分而治之的思想和岛屿迁徙模型的径向基神经网络集成的新方法。实验结果表明,岛屿迁徙神经网络集成预测模型不但可以提高系统对多维空间的高维搜索能力,简化网络结构,而且在产品的自动化检测试验中也可获得更高的预测精度。

RBF networks ensemble based on island migrating model and research of its application

Neural network ensemble is a very popular learning paradigm where the outputs of a set of separately trained neural network are combined to form one unified prediction,and it can significantly improve the generalization ability of the learning systems.To improve the effectiveness of ensemble,a RBF networks ensemble based on the divided and ruled thought and an island migrating model is presented in this paper.Experimental results show that the predictive model of neural networks ensemble based on an island migrating model can not only raise the system’s searching ability of high dimension for the much dimension space,simplify the structure of the networks,but also can gain higher predicting accuracy in the automatic examination．