Identification of Hammerstein model using functional link artificial neural network

In this paper, a novel algorithm is developed for identifying Hammerstein model. The static nonlinear function is characterized by function link artificial neural network (FLANN) and the linear dynamic subsystem by an ARMA model. The utilization of FLANN can not only result in a simple and effective representation of static nonlinearity but also simplify the learning algorithm. A two-step procedure is adopted to identify Hammerstein model by using a specially designed input signal, which separates the identification of linear part from that of nonlinear part. Levenberg-Marquart algorithm is used to learn the weights of FLANN. Simulation examples demonstrate the effectiveness of the proposed method.

[1]  Oliver Nelles,et al.  Nonlinear internal model control based on local linear neural networks , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[2]  Feng Ding,et al.  An efficient hierarchical identification method for general dual-rate sampled-data systems , 2014, Autom..

[3]  Enrique Baeyens,et al.  Identification of block-oriented nonlinear systems using orthonormal bases , 2004 .

[4]  Johan A. K. Suykens,et al.  Identification of MIMO Hammerstein models using least squares support vector machines , 2005, Autom..

[5]  G. L. Sicuranza,et al.  A Generalized FLANN Filter for Nonlinear Active Noise Control , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[7]  Tolgay Kara,et al.  Nonlinear modeling and identification of a spark ignition engine torque , 2012 .

[8]  V. Chandrasekar,et al.  Hammerstein model identification by multilayer feedforward neural networks , 1997, Int. J. Syst. Sci..

[9]  F. Ding,et al.  Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle , 2014 .

[10]  Johan A. K. Suykens,et al.  Least-Squares Support Vector Machines for the Identification of Wiener-Hammerstein Systems , 2012 .

[11]  Shuzhi Sam Ge,et al.  Iterative Identification of Neuro-Fuzzy-Based Hammerstein Model with Global Convergence , 2005 .

[12]  Hamed Mojallali,et al.  Identification of multiple-input single-output Hammerstein models using Bezier curves and Bernstein polynomials , 2011 .

[13]  Sreejit Chakravarty,et al.  A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices , 2012, Appl. Soft Comput..

[14]  Er-Wei Bai.  An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems , 1998, Autom..

[15]  K. Narendra,et al.  An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .

[16]  Eric Rogers,et al.  Recursive identification of Hammerstein systems with application to electrically stimulated muscle , 2012 .

[17]  S. Hwang,et al.  Iterative Identification of Continuous-Time Hammerstein and Wiener Systems Using a Two-Stage Estimation Algorithm , 2009 .

[18]  Nguyen Trong Tai,et al.  A hysteresis functional link artificial neural network for identification and model predictive control of SMA actuator , 2012 .

[19]  Shuzhi Sam Ge,et al.  A noniterative neuro-fuzzy based identification method for Hammerstein processes , 2005 .

[20]  Qinyu. Zhu.  Extreme Learning Machine , 2013 .

[21]  Er-Wei Bai,et al.  Decoupling the linear and nonlinear parts in Hammerstein model identification , 2004, Autom..

[22]  Sheng Chen,et al.  The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization , 2012, Neurocomputing.

[23]  Syuan-Yi Chen,et al.  Recurrent Functional-Link-Based Fuzzy Neural Network Controller With Improved Particle Swarm Optimization for a Linear Synchronous Motor Drive , 2009, IEEE Transactions on Magnetics.

[24]  Jiashu Zhang,et al.  Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel , 2009, IEEE Transactions on Neural Networks.

[25]  Feng Ding,et al.  Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle , 2013, Inf. Sci..

[26]  Ganapati Panda,et al.  Improved identification of Hammerstein plants using new CPSO and IPSO algorithms , 2010, Expert Syst. Appl..

[27]  Feng Ding,et al.  Combined state and least squares parameter estimation algorithms for dynamic systems , 2014 .

[28]  Ganapati Panda,et al.  Development and performance evaluation of FLANN based model for forecasting of stock markets , 2009, Expert Syst. Appl..

[29]  Feng Ding,et al.  Least squares based and gradient based iterative identification for Wiener nonlinear systems , 2011, Signal Process..

[30]  Jozef Vörös,et al.  Iterative algorithm for parameter identification of Hammerstein systems with two-segment nonlinearities , 1999, IEEE Trans. Autom. Control..

[31]  Yaping Dai,et al.  Identification of Hammerstein LSSVM-ARMAX systems and its application in Continuous Stirred Tank Reactor , 2011 .

[32]  Guangyi Cao,et al.  Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines , 2008 .

[33]  Raymond A. de Callafon,et al.  Closed-Loop Identification of Hammerstein Systems Using Iterative Instrumental Variables , 2011 .

[34]  R. Luus,et al.  A noniterative method for identification using Hammerstein model , 1971 .

[35]  F. Ding,et al.  Newton iterative identification for a class of output nonlinear systems with moving average noises , 2013 .

[36]  F. Ding,et al.  Least squares algorithm for an input nonlinear system with a dynamic subspace state space model , 2014 .

[37]  S. Sung.  System Identification Method for Hammerstein Processes , 2002 .

[38]  Ganapati Panda,et al.  Development of immunized pso algorithm and its application to hammerstein model identification , 2009, 2009 IEEE Congress on Evolutionary Computation.

[39]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[40]  Alex ChiChung Kot,et al.  Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[41]  Weisi Lin,et al.  Hammerstein model identification based on bacterial foraging , 2006 .

[42]  Feng Ding,et al.  Identification methods for Hammerstein nonlinear systems , 2011, Digit. Signal Process..

[43]  Sung-Bae Cho,et al.  An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification , 2012, J. Syst. Softw..

[44]  Feng Ding,et al.  Hierarchical Least Squares Estimation Algorithm for Hammerstein–Wiener Systems , 2012, IEEE Signal Processing Letters.