An efficient parameter estimation method for nonlinear high-order systems via surrogate modeling and cuckoo search

[1]  Hong Wang,et al.  POD/DEIM Reduced-Order Modeling of Time-Fractional Partial Differential Equations with Applications in Parameter Identification , 2016, J. Sci. Comput..

[2]  Yongguang Yu,et al.  An Effective Hybrid Cuckoo Search Algorithm for Unknown Parameters and Time Delays Estimation of Chaotic Systems , 2018, IEEE Access.

[3]  Alfio Quarteroni,et al.  Efficient State/Parameter Estimation in Nonlinear Unsteady PDEs by a Reduced Basis Ensemble Kalman Filter , 2017, SIAM/ASA J. Uncertain. Quantification.

[4]  Nithin V. George,et al.  Parameter estimation of MIMO bilinear systems using a Levy shuffled frog leaping algorithm , 2017, Soft Comput..

[5]  Yan Chen,et al.  Restricted gene expression programming: a new approach for parameter identification inverse problems of partial differential equation , 2015, Soft Computing.

[6]  Razvan Stefanescu,et al.  Comparative numerical analysis using reduced-order modeling strategies for nonlinear large-scale systems , 2017, J. Comput. Appl. Math..

[7]  Shenquan Wang,et al.  Distributed H∞ consensus control for nonlinear multi-agent systems under switching topologies via relative output feedback , 2017, Neural Computing and Applications.

[8]  Xin-Ming Zhang,et al.  Parameter estimation of shallow wave equation via cuckoo search , 2017, Neural Computing and Applications.

[9]  B. Kaltenbacher,et al.  An online parameter identification method for time dependent partial differential equations , 2015, 1506.00658.

[10]  Xin-She Yang,et al.  Attraction and diffusion in nature-inspired optimization algorithms , 2019, Neural Computing and Applications.

[11]  Jun Wang,et al.  A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation , 2016, Neural Computing and Applications.

[12]  Jun Wang,et al.  A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation , 2015, Neural Computing and Applications.

[13]  Tarun Kumar Rawat,et al.  Optimal design of FIR fractional order differentiator using cuckoo search algorithm , 2015, Expert Syst. Appl..

[14]  Paul G. Constantine,et al.  Active Subspaces - Emerging Ideas for Dimension Reduction in Parameter Studies , 2015, SIAM spotlights.

[15]  Adrian Sandu,et al.  POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation , 2014, J. Comput. Phys..

[16]  Mario Ohlberger,et al.  Data-driven combined state and parameter reduction for inverse problems , 2014, Advances in Computational Mathematics.

[17]  Xin-She Yang,et al.  Swarm Intelligence and Evolutionary Computation: Overview and Analysis , 2015, Recent Advances in Swarm Intelligence and Evolutionary Computation.

[18]  H. V. D. Vorst,et al.  Model Order Reduction: Theory, Research Aspects and Applications , 2008 .

[19]  Jun Wang,et al.  Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm. , 2014, Chaos.

[20]  Tiangang Cui,et al.  Data‐driven model reduction for the Bayesian solution of inverse problems , 2014, 1403.4290.

[21]  Xin-She Yang,et al.  Cuckoo search: recent advances and applications , 2013, Neural Computing and Applications.

[22]  Xin-She Yang,et al.  Bat algorithm based on simulated annealing and Gaussian perturbations , 2014, Neural Computing and Applications.

[23]  B. R. Noack.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 2013 .

[24]  Razvan Stefanescu,et al.  Application of POD-DEIM Approach for Dimension Reduction of a Diffusive Predator-Prey System with Allee Effect , 2013, LSSC.

[25]  Jiguo Cao,et al.  Parameter Estimation of Partial Differential Equation Models , 2013, Journal of the American Statistical Association.

[26]  Bo Han,et al.  A wavelet multiscale iterative regularization method for the parameter estimation problems of partial differential equations , 2013, Neurocomputing.

[27]  Xin-She Yang,et al.  Cuckoo Search for Inverse Problems and Topology Optimization , 2013 .

[28]  Thomas Peters,et al.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry, 2nd edn., by P. Holmes , 2012 .

[29]  Yin Ming-hao,et al.  Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method , 2012 .

[30]  P. Holmes,et al.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry: OTHER APPLICATIONS AND RELATED WORK , 2012 .

[31]  C. Farhat,et al.  Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations , 2011 .

[32]  A. Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[33]  Danny C. Sorensen,et al.  Nonlinear Model Reduction via Discrete Empirical Interpolation , 2010, SIAM J. Sci. Comput..

[34]  Karen Willcox,et al.  Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems , 2010, SIAM J. Sci. Comput..

[35]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[36]  David Galbally,et al.  Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems , 2009 .

[37]  Peter Benner,et al.  Dimension Reduction of Large-Scale Systems , 2005 .

[38]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[39]  N. Nguyen,et al.  An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .

[40]  Jens Timmer,et al.  Parameter Identification Techniques for Partial Differential Equations , 2004, Int. J. Bifurc. Chaos.

[41]  P. Astrid,et al.  Fast reduced order modeling technique for large scale LTV systems , 2004, Proceedings of the 2004 American Control Conference.

[42]  J. Timmer,et al.  Fitting parameters in partial differential equations from partially observed noisy data , 2002 .

[43]  Stefan Volkwein,et al.  Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics , 2002, SIAM J. Numer. Anal..

[44]  Lawrence Sirovich,et al.  Karhunen–Loève procedure for gappy data , 1995 .