摘要
以设计最小RBF网络结构为着眼点,提出了一种基于互信息的RBF神经网络结构优化算法。该算法用k近邻统计法估计隐节点输出矩阵与输出节点输出矩阵之间的互信息,获得每个隐节点与输出节点之间的相关性度量,删除相关性最小的隐节点,进而达到优化网络结构的目的。该算法具有自恢复机制,在简化网络结构的同时能有效保证网络的信息处理能力。在人工数据集和真实基准数据集上的仿真实验验证了该算法的有效性与稳定性。
Aiming at designing the simplest RBF neural network architecture, a RBF neural network structure design al- gorithm based on mutual information was proposed in this paper. The relevance measure between each hidden and out- put units can be acquired by estimating the mutual information between the output matrix of the hidden unit and output unit, using k-nearest-neighbor statistics. And the simplest RBF neural network architecture can be achieved by removing the least related hidden units from the trained neural network one after another according to the relevance measure. This algorithm has the self-recovery mechanism, and the information processing capacity of the neural network can be en- sured in the process of the simplification of the network＇s architecture. The simulation results on the artificial datasets and the real-world benchmark datasets show the effectiveness and stability of the algorithm.
出处
《计算机科学》
CSCD
北大核心
2013年第6期252-255,271,共5页
Computer Science
基金
国家自然科学基金(60971048)
辽宁省科技厅基金(2009S051)资助
关键词
RBF神经网络
结构优化
k-近邻统计法
互信息
RBF neural network, Structural optimization, k-nearest-neighbor statistics, Mutual information
