改进生物地理学算法辨识Hammerstein模型  

Identification of Hammerstein Model Based on Improved Biogeography-Based Optimization Algorithm

作　　者：罗丹[1] 张宏立[1] 

机构地区：[1]新疆大学电气工程学院,新疆乌鲁木齐830047

出　　处：《计算机仿真》2014年第5期342-345,共4页Computer Simulation

基　　金：新疆维吾尔自治区自然科学基金(2012211A003)

摘　　要：为了研究非线性控制的辨识优化问题,提出辨识非线性模型的新方法。针对传统辨识方法对非线性模型中非线性部分辨识精度低、效果差等缺点,将生物地理学算法引入到非线性辨识中,并结合Hammerstein模型,提出了一种基于生物地理学算法的Hammerstein模型识别方法。为了进一步增强生物地理学算法的辨识精度,采用了带有加权因子的迁移和基于柯西变异的改进生物地理学算法对非线性系统进行辨识。通过数值仿真,并与传统的最小二乘法和基本生物地理学算法进行比较,结果表明利用改进算法能够提高辨识精度,验证了改进算法的有效性和可行性。A new parameter identification method for nonlinear model was proposed. In order to overcome the disadvantage that the recognition accuracy for the nonlinear part of nonlinear model is poor in traditional identification methods, the Biogeography-Based Optimization （BBO） was introduced to nonlinear system identification and combined Hammerstein model, and the Hammerstein model identification method based on BBO algorithm was proposed. In order to enhance the identification performance of the BBO algorithm, the BBO was improved based on partial mi- gration with weight factor and the Cauchy mutation was used to identify nonlinear system. According to a numerical simulation arid comparing with the traditional least square method and the basic BBO algorithm, the results show that the algorithm can improved the identification accuracy and obtain a good recognition results, which verifies the effectiveness and feasibility of the algorithm.

关 键 词：生物地理学优化算法 系统辨识 海默斯坦模型 

分 类 号：TP202.7[自动化与计算机技术—检测技术与自动化装置]