Name
Abstract | ||
|---|---|---|
This paper presents a parameter identification method of nonlinear Hammerstein model with two-segment piecewise nonlinearities. Its basic idea is that: First of all, expressing the output of the Hammerstein nonlinear models as a regressive equation in all parameters based on the key term separation principle and separating key term from linear block and nonlinear block. Then, the unknown true outputs in the information vector are replaced with the outputs of an auxiliary model, the unknown internal variables and the unmeasured noise terms are replaced with the estimated internal variables and the estimated residuals, respectively. Accordingly, the problem of the nonlinear system identification is cast as function optimization problem over parameter space; a particle swarm optimization (PSO) algorithm is adopted to solve the optimization problem. In order to further enhance the precision and robust of identification, an improved particle swarm optimization (IPSO) algorithm is applied to search the parameter space to find the optimal estimation of the system parameters. Finally, the feasibility and efficiency of the presented algorithm are demonstrated using numerical simulations. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BICTA.2010.5645355 | BIC-TA |
Keywords | Field | DocType |
key term separation principle,regressive equation,hammerstein,identification,nonlinear system identification,parameter space searching,parameter estimation,information vector,regression analysis,pso,two-segment piecewise nonlinearity,particle swarm optimisation,nonlinear hammerstein model,nonlinear systems,search problems,nonlinear block,numerical simulation,function optimization problem,parameter identification,particle swarm optimization,regression equation,robustness,separation principle,parameter space,optimization problem,optimal estimation,computational modeling | Nonlinear system,Separation principle,Computer science,Control theory,Artificial intelligence,Estimation theory,Optimization problem,Piecewise,Particle swarm optimization,Mathematical optimization,Algorithm,Nonlinear system identification,Multi-swarm optimization,Machine learning | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4244-6437-1 |
Citations | PageRank | References |
1 | 0.43 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Wang | 1 | 8 | 1.93 |
| Keyi Xing | 2 | 522 | 34.59 |
| Xiaoping Xu | 3 | 16 | 2.01 |
| Huixia Liu | 4 | 200 | 7.27 |
| Xiaojing Sun | 5 | 1 | 1.11 |