[1]
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Liu Y, Bai E W. Iterative identification of Hammerstein systems. Automatica, 2007, 43(2): 346-354[2] Gui Wei-Hua, Song Hai-Ying, Yang Chun-Hua. Hammer-stein-Wiener model identified by least-squares-support-vector machine and its application. Control Theory and Applications, 2008, 25(3): 393-397(桂卫华, 宋海鹰, 阳春华. Hammerstein-Wiener模型最小二乘向量机辨识及其应用. 控制理论与应用, 2008, 25(3): 393-397)[3] Marzban H R, Tabrizidooz H R, Razzaghi M. A composite collocation method for the nonlinear mixed Volterra-Fredholm-Hammerstein integral equations. Communications in Nonlinear Science and Numerical Simulation, 2011, 16(3): 1186-1194[4] Chen H T, Hwang S H, Chang C T. Iterative identification of continuous-time Hammerstein and Wiener systems using a two-stage estimation algorithm. Industrial and Engineering Chemistry Research, 2009, 48(3): 1495-1510[5] Wang Feng, Xing Ke-Yi, Xu Xiao-Ping. Study on method for identification of Hammerstein model. Journal of System Simulation, 2011, 23(6): 1090-1092, 1136(王峰, 邢科义, 徐小平. 辨识Hammerstein模型方法研究. 系统仿真学报, 2011, 23(6): 1090-1092, 1136)[6] Ding Feng, Liu Jing-Fan, Xiao Yong-Song. Parameter estimation for a class of nonlinear systems. Control of Engineering China, 2011, 18(3): 373-376, 409 (丁峰, 刘景璠, 肖永松. 一类非线性系统的参数估计. 控制工程, 2011, 18(3): 373-376, 409)[7] Hou Zhong-Sheng, Xu Jian-Xin. On data-driven control theory: the state of the art and perspective. Acta Automatica Sinica, 2009, 35(6): 650-667(侯忠生, 许建新. 数据驱动控制理论及方法的回顾和展望. 自动化学报, 2009, 35(6): 650-667)[8] Bai E W. An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems. Automatica, 1998, 34(3): 333-338[9] Wang D Q, Ding F. Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems. Computers and Mathematics with Applications, 2008, 56(12): 3157-3164[10] Zhu Y C. Estimation of an N-L-N Hammerstein-Wiener model. Automatica, 2002, 38(9): 1607-1614[11] Crama P, Schoukens J. Hammerstein-Wiener system estimator initialization. Automatica, 2004, 40(9): 1543-1550[12] Park H C, Sung S W, Lee J. Modeling of Hammerstein-Wiener processes with special input test signals. Industrial and Engineering Chemistry Research, 2006, 45(3): 1029- 1038[13] Man Hong, Shao Cheng. Neural network predictive control of continuous stirred-tank reactor based on Hammerstein-Wiener model. CIESC Journal, 2011, 62(8): 2275-2280(满红, 邵诚. 基于Hammerstein-Wiener模型的连续搅拌反应釜神经网络预测控制. 化工学报, 2011, 62(8): 2275-2280)[14] Li Yan, Mao Zhi-Zhong, Wang Yan, Yuan Ping. Fuzzy predictive control of Hammerstein-Wiener nonlinear systems. Journal of Northeastern University (Natural Science), 2011, 32(3): 322-326(李妍, 毛志忠, 王琰, 袁平. Hammerstein-Wiener非线性系统的模糊预测控制. 东北大学学报(自然科学版), 2011, 32(3): 322- 326)[15] Li Yan, Mao Zhi-Zhong, Wang Yan, Yuan Ping, Jia Ming-Xing. Predictive control of Hammerstein-Wiener nonlinearity based on polytopic terminal region. Acta Automatica Sinica, 2011, 37(5): 629-638(李妍, 毛志忠, 王琰, 袁平, 贾明兴. 基于多面体终端域的Hammerstein-Wiener非线性预测控制. 自动化学报, 2011, 37(5): 629-638)[16] Wang Wei, Chai Tian-You, Zhao Li-Jie. Dynamic partial least squares modeling with recurrent neural networks of stable learning. Control Theory and Applications, 2012, 29(3): 337-341(王魏, 柴天佑, 赵立杰. 带有稳定学习的递归神经网络动态偏最小二乘建模. 控制理论与应用, 2012, 29(3): 337-341)[17] Jia L, Chiu M S, Ge S S. A noniterative neuro-fuzzy based identification method for Hammerstein processes. Journal of Process Control, 2005, 15(7): 749-761[18] Hahn J, Edgar T F. A Gramian based approach to nonlinearity quantification and model classification. Industrial and Engineering Chemistry Research, 2001, 40(24): 5724-5731
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