A novel APSO-aided weighted LSSVM method for nonlinear Hammerstein system identification

DOI10.1016/j.jfranklin.2016.12.022zbMath1378.93152OpenAlexW2563387527MaRDI QIDQ1691186

Liang Ma, Xinggao Liu

Publication date: 15 January 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.12.022

zbMATH Keywords

parameter estimationHammerstein systemSVMRLSadaptive particle swarm optimization algorithmAPSOWLSVMevolutionary state estimation

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Least squares and related methods for stochastic control systems (93E24) Optimal stochastic control (93E20)

Related Items (5)

Nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory ⋮ A novel mesh discretization strategy for numerical solution of optimal control problems in aerospace engineering ⋮ A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems ⋮ Iterative identification algorithms for bilinear-in-parameter systems with autoregressive moving average noise ⋮ Maximum likelihood based identification methods for rational models

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