基于LM算法的在线自适应RBF网结构优化算法

针对LM算法不能在线训练RBF网络以及RBF网络结构设计算法中存在的问题,提出一种基于LM算法的在线自适应RBF网络结构优化算法.该算法引入滑动窗口和在线优化网络结构的思想,滑动窗口的引入既使得LM算法能够在线训练RBF网络,又使得网络对学习参数的变化具有更好的鲁棒性,并且易于收敛.在线优化网络结构使得网络在学习过程中能够根据训练样本的训练误差和隐节点的相关信息,在线自适应调整网络结构,跟踪非线性时变系统的变化,使网络维持最为紧凑的结构,以保证网络的泛化性能.最后通过仿真实验验证了所提出算法的性能.

Online self-adaptive optimal algorithm for RBF network based on Levenberg-Marquardt algorithm

Aming at the problem that the Levenberg-Marquardt(LM) algorithm can not online train RBF network and the problem in RBF network structure design methods, this paper presents an online self-adaptive RBF network structure design method based on the LM algorithm. The ideal of sliding window and online structure optimization are introduced in this algorithm, the introduction of sliding window enables the RBF network to be trained online by the LM algorithm, and makes the RBF network more robust to the changes of the learning parameters and is easy to converge. The online structure optimization can online self-adaptive adjust the structure of RBF network based on the information of training errors and hidden unites to track the time-varying systems, which helps to maintain a compact netowrk and satisfactory generation. Finally, the experiment results show the performance of the proposed algorithm.