基于ROLS算法的递归RBF神经网络结构设计  

Structure design for recurrent RBF neural network based on recursive orthogonal least squares

作　　者：乔俊飞 马士杰 杨翠丽 QIAO Junfei;MA Shijie;YANG Cuili(Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China)

机构地区：[1]北京工业大学信息学部,北京100124 [2]计算智能与智能系统北京市重点实验室,北京100124

出　　处：《化工学报》2018年第3期1191-1199,共9页CIESC Journal

基　　金：国家自然科学基金项目(61533002;61603012);北京市教育委员会科研计划项目(KM201710005025)~~

摘　　要：针对递归RBF神经网络结构难以自适应问题,提出一种基于递归正交最小二乘(recursive orthogonal least squares,ROLS)算法的结构设计方法。首先,利用ROLS算法来计算隐含层神经元的独立贡献度和损失函数,以此判断增加或归为不活跃组的神经元,同时调整神经网络的拓扑结构,并且利用奇异值分解(singular value decomposition,SVD)决定最佳的隐含层神经元个数,以此来删除不活跃组中相对不活跃的神经元,有效地解决了递归RBF神经网络结构冗余和难以自适应问题。其次,利用梯度下降算法更新递归RBF神经网络的参数来保证神经网络的精度。最后,通过对Mackey-Glass时间序列预测、非线性系统辨识和污水处理过程中关键水质参数动态建模,证明了该结构设计方法的可行性和有效性。Aiming at the problem of recurrent radial basis function(RRBF)neural network structure which isdifficult to be self-adaptive,this paper proposes a structure design method based on recursive orthogonal least square(ROLS)algorithm.Firstly,ROLS algorithm is used to calculate the contribution and the loss function of hiddenlayer neurons,which determines to increase or be grouped into inactive neurons,and the topology structure of neuralnetwork is adjusted accordingly.At the same time,singular value decomposition(SVD)is applied to determine thebest number of hidden layer neurons in order to delete the neurons of the inactive group,which effectively solvesthe problems of RRBF neural network structure which is redundant and hardly self-adaptive.Secondly,the gradientdescent algorithm is utilized to update the parameters of RRBF neural network in order to ensure the accuracy ofneural network.Finally,several experiments including the Mackey-Glass time series prediction,nonlinear systemidentification and key water quality parameters dynamic modeling in wastewater treatment process are conducted,and the simulation results prove the feasibility and effectiveness of the structure design method.

关 键 词：神经网络 结构设计 算法 奇异值分解 动态建模 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]