Identification of Neuro-Fuzzy Hammerstein Model Based on Probability Density Function  

作　　者：方甜莲 贾立 

机构地区：[1]Shanghai Key Laboratory of Power Station Automation Technology,College of Mechatronics Engineering and Automation,Shanghai University

出　　处：《Journal of Donghua University(English Edition)》2016年第5期703-707,共5页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.61374044);Shanghai Municipal Science and Technology Commission,China(No.15510722100);Shanghai Municipal Education Commission,China(No.14ZZ088);Shanghai Talent Development Plan,China;Shanghai Baoshan Science and Technology Commission,China(No.bkw2013120)

摘　　要：A new identification method of neuro-uzzy Hammerstein model based on probability density function(PDF) is presented,which is different from the idea that mean squared error(MSE) is employed as the index function in traditional identification methods.Firstly,a neuro-fuzzy based Hammerstein model is constructed to describe the nonlinearity of Hammerstein process without any prior process knowledge.Secondly,a kind of special test signal is used to separate the link parts of the Hammerstein model.More specifically,the conception of PDF is introduced to solve the identification problem of the neuro-fuzzy Hammerstein model.The antecedent parameters are estimated by a clustering algorithm,while the consequent parameters of the model are identified by designing a virtual PDF control system in which the PDF of the modeling error is estimated and controlled to converge to the target.The proposed method not only guarantees the accuracy of the model but also dominates the spatial distribution of PDF of the model error to improve the generalization ability of the model.Simulated results show the effectiveness of the proposed method.A new identification method of neuro-uzzy Hammerstein model based on probability density function（PDF） is presented,which is different from the idea that mean squared error（MSE） is employed as the index function in traditional identification methods.Firstly,a neuro-fuzzy based Hammerstein model is constructed to describe the nonlinearity of Hammerstein process without any prior process knowledge.Secondly,a kind of special test signal is used to separate the link parts of the Hammerstein model.More specifically,the conception of PDF is introduced to solve the identification problem of the neuro-fuzzy Hammerstein model.The antecedent parameters are estimated by a clustering algorithm,while the consequent parameters of the model are identified by designing a virtual PDF control system in which the PDF of the modeling error is estimated and controlled to converge to the target.The proposed method not only guarantees the accuracy of the model but also dominates the spatial distribution of PDF of the model error to improve the generalization ability of the model.Simulated results show the effectiveness of the proposed method.

关 键 词：Probability clustering guarantees separate converge prior generalization conception squared nonlinearity 

分 类 号：TP371[自动化与计算机技术—计算机系统结构]