Abstract: Due to many classical identification methods cannot be directly used for closed-loop control system, an improved identification method is proposed to simultaneously identify model parameters and the structure. The improved identification method used the genetic algorithm to estimate the initial search scope for the PSO algorithm, and then used the search result as the initial value of the Rosenbrock algorithm. On the basis the genetic algorithm to estimate is introduced to provide the rough initial search scope for the presented algorithm to improve the validity and accuracy. Simulation results show that compare with the PSO algorithm, the inertia weight variation PSO algorithm and the PSO-SQP algorithm proposed by Qibing Jin et a...
Evolutionary algorithm (EA) such as genetic algorithm (GA) has demonstrated to be an effective metho...
In order to use existing identification tools effectively, a user must make critical choices a prior...
This paper develops a genetic algorithm based technique that may be used to identify multivariable s...
This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline a...
Abstract: Estimate model parameter and the structure simultaneously is a crucial and challenging pro...
This paper presents a methodology for finding optimal system parameters and optimal control paramete...
This paper is concerned with the parameter identification problem for chaotic dynamic systems. An im...
A modified versions of metaheuristic algorithms are presented to compare their performance in identi...
Current online identification techniques are recursive and involve local search techniques. In this...
This work presents a recursive identification algorithm. This algorithm relates to the identificatio...
In this study, a novel easy-to-use meta-heuristic method for simultaneous identification of model st...
Abstract: The current paper presents an adaptive system identification/parameter estimation algorith...
The development of a multivariable system identification model for dynamic discrete-time nonlinear s...
Parameter identification of robot manipulators is an indispensable pivotal process of achieving accu...
This paper considers the parameter identification of Wiener systems with colored noise. The difficul...
Evolutionary algorithm (EA) such as genetic algorithm (GA) has demonstrated to be an effective metho...
In order to use existing identification tools effectively, a user must make critical choices a prior...
This paper develops a genetic algorithm based technique that may be used to identify multivariable s...
This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline a...
Abstract: Estimate model parameter and the structure simultaneously is a crucial and challenging pro...
This paper presents a methodology for finding optimal system parameters and optimal control paramete...
This paper is concerned with the parameter identification problem for chaotic dynamic systems. An im...
A modified versions of metaheuristic algorithms are presented to compare their performance in identi...
Current online identification techniques are recursive and involve local search techniques. In this...
This work presents a recursive identification algorithm. This algorithm relates to the identificatio...
In this study, a novel easy-to-use meta-heuristic method for simultaneous identification of model st...
Abstract: The current paper presents an adaptive system identification/parameter estimation algorith...
The development of a multivariable system identification model for dynamic discrete-time nonlinear s...
Parameter identification of robot manipulators is an indispensable pivotal process of achieving accu...
This paper considers the parameter identification of Wiener systems with colored noise. The difficul...
Evolutionary algorithm (EA) such as genetic algorithm (GA) has demonstrated to be an effective metho...
In order to use existing identification tools effectively, a user must make critical choices a prior...
This paper develops a genetic algorithm based technique that may be used to identify multivariable s...