基于粒子群优化与SVR-ADLA算法的MIMO系统识别研究  

Identification of MIMO Systems based on particle swarm optimization,support vector regression and annealing dynamic learning

作　　者：杨发权[1] 李赞[2] 郝本建[2] 樊耘[1] 

机构地区：[1]佛山科学技术学院电子与信息工程学院,佛山528000 [2]西安电子科技大学综合业务网理论及关键技术国家重点实验室,西安710071

出　　处：《高技术通讯》2015年第1期24-30,共7页Chinese High Technology Letters

基　　金：国家自然科学基金(61072070;61301179);教育部博士学科点基金(20110203110011);教育部基础科研业务费(72124338);陕西省自然基金重点项目(2012JZ8002);高等学校学科创新引智计划(B08038)资助项目

摘　　要：针对现有基于径向基函数(RBF)网络对多输入多输出(MIMO)系统识别中存在收敛速度慢、系统识别稳定性不高的问题,提出了一种新的MIMO系统识别方法:采用支持向量回归(SVR)算法建立RBF网络初始化结构,确定初始化网络参数;采用退火动力学习(ADL)算法对系统识别网络进行训练,在训练过程中采用粒子群优化(PSO)迭代算法选出最佳学习率组合,使识别网络实现对MIMO系统的识别。对一个两输入输出系统进行了识别仿真,仿真结果表明,用该识别方法重建的识别系统性能优于目前RBF网络参数优化过程中常用的最小平方算法或梯度下降法算法。RBF网络识别系统易于实现,在MIMO系统识别中具有广泛的应用前景。The identification of a multiple-input multiple-out-put （MIMO） system based on a radial basis function （RBF） network was studied, and a new identification method was put forward to solve the problems of slow conver- gence rate and low identification stability in current identification. The new method uses the support vector regres- sion （SVR） algorithm to establish the initialized structure of a RBF network and set the parameters of the initialized network, and then adopts the annealing dynamic learning （ADL） algorithm to train the system＇ s identification net- work, and in the training, uses the iteration of particle swarm optimization （PSO） to select the best learning rate combination to make the MIMO system recognized by the identification network. The identification simulation for a two-input and two-output, system was conducted, and the simulation results showed that the system identification performance of the proposed method was better than the least square algorithm and the gradient descent algorithm frequently-used in the current process of optimizing RBF network parameters. The identification system based on a RBF network is easy to implement and it has a wide application prospect in MIMO system identification.

关 键 词：多输入多输出(MIMO)系统识别 径向基函数(RBF)网络 支持向量回归(SVR) 退火动力学习(ADL) 粒子群优化(PSO) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]