Prediction and simulation errors in parameter estimation for nonlinear systems

[1]  P. Young.  The use of linear regression and related procedures for the identification of dynamic processes , 1968 .

[2]  John J. Grefenstette,et al.  Genetic algorithms and their applications , 1987 .

[3]  J. P. Norton,et al.  An Introduction to Identification , 1986 .

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[5]  Petre Stoica,et al.  On the uniqueness of prediction error models for systems with noisy input-output data , 1987, Autom..

[6]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[9]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[10]  Sheng Chen,et al.  Identification of non-linear rational systems using a prediction-error estimation algorithm , 1989 .

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[13]  S. Billings,et al.  Rational model identification using an extended least-squares algorithm , 1991 .

[14]  S. Billings,et al.  Recursive Parameter Estimation for Nonlinear Rational Models , 1991 .

[15]  L. A. Aguirre,et al.  Validating Identified Nonlinear Models with Chaotic Dynamics , 1994 .

[16]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[17]  S. A. Billings,et al.  Rational model data smoothers and identification algorithms , 1997 .

[18]  S. Billings,et al.  On Overparametrization of Nonlinear Discrete Systems , 1997 .

[19]  Ricardo H. C. Takahashi,et al.  Improving generalization of MLPs with multi-objective optimization , 2000, Neurocomputing.

[20]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  Michel Gevers,et al.  Modelling, Identification and Control , 2002 .

[23]  Ricardo H. C. Takahashi,et al.  A multiobjective methodology for evaluating genetic operators , 2003 .

[24]  L. Piroddi,et al.  An identification algorithm for polynomial NARX models based on simulation error minimization , 2003 .

[25]  Demosthenis D. Rizos,et al.  Identification of pre-sliding friction dynamics. , 2004, Chaos.

[26]  Jürgen Kurths,et al.  Nonlinear Dynamical System Identification from Uncertain and Indirect Measurements , 2004, Int. J. Bifurc. Chaos.

[27]  Peter J. Fleming,et al.  Evolution of mathematical models of chaotic systems based on multiobjective genetic programming , 2005, Knowledge and Information Systems.

[28]  Ferenc Szeifert,et al.  Genetic programming for the identification of nonlinear input-output models , 2005 .

[29]  Quanmin Zhu.  An implicit least squares algorithm for nonlinear rational model parameter estimation , 2005 .

[30]  Carlo Novara,et al.  Model quality in identification of nonlinear systems , 2005, IEEE Transactions on Automatic Control.

[31]  Leonardo A. B. Tôrres,et al.  Evaluation of dynamical models: dissipative synchronization and other techniques. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Wei-Der Chang,et al.  An improved real-coded genetic algorithm for parameters estimation of nonlinear systems , 2006 .

[33]  George W. Irwin,et al.  Prediction- and simulation-error based perceptron training: Solution space analysis and a novel combined training scheme , 2007, Neurocomputing.

[34]  Leonardo A. B. Tôrres.  Discrete-time dynamic systems synchronization: Information transmission and model matching , 2007 .

[35]  Keith Worden,et al.  Genetic algorithm with an improved fitness function for (N)ARX modelling , 2007 .

[36]  Ricardo H. C. Takahashi,et al.  Multiobjective parameter estimation for non-linear systems: affine information and least-squares formulation , 2007, Int. J. Control.

[37]  Ricardo H. C. Takahashi,et al.  Multi-objective parameter estimation via minimal correlation criterion , 2007 .

[38]  Quan Min Zhu,et al.  Development of omni-directional correlation functions for nonlinear model validation , 2007, Autom..

[39]  Quanmin Zhu,et al.  An enhanced back propagation algorithm for parameter estimation of rational models , 2008, Int. J. Model. Identif. Control..

[40]  Luigi Piroddi,et al.  Simulation error minimisation methods for NARX model identification , 2008, Int. J. Model. Identif. Control..

[41]  Xavier Blasco Ferragud,et al.  Non-linear robust identification using evolutionary algorithms: Application to a biomedical process , 2008, Eng. Appl. Artif. Intell..

[42]  Stephen A. Billings,et al.  Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information , 2008, Int. J. Model. Identif. Control..

[43]  Antônio de Pádua Braga,et al.  A multi-objective approach to RBF network learning , 2008, Neurocomputing.

[44]  Marcello Farina,et al.  Some convergence properties of multi-step prediction error identification criteria , 2008, 2008 47th IEEE Conference on Decision and Control.

[45]  Sheng Chen,et al.  Model selection approaches for non-linear system identification: a review , 2008, Int. J. Syst. Sci..

[46]  Stephen A. Billings,et al.  Improved parameter estimates for non-linear dynamical models using a bootstrap method , 2009, Int. J. Control.

[47]  L. Coelho,et al.  Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach , 2009 .

[48]  T. K. Radhakrishnan,et al.  Real-coded genetic algorithm for system identification and controller tuning , 2009 .

[49]  Stephen A. Billings,et al.  Model Estimation of Cerebral Hemodynamics Between Blood Flow and Volume Changes: A Data-Based Modeling Approach , 2009, IEEE Transactions on Biomedical Engineering.

[50]  Barnabás Póczos,et al.  Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques , 2009, J. Mach. Learn. Res..