Identification of Wiener Model Using Least Squares Support Vector Machine Optimized by Adaptive Particle Swarm Optimization

Ma, Jun; Zhao, Liheng; Han, Zhenzhen; Tang, Yinggan

doi:10.1007/s40313-015-0207-1

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…This adaptation to the environment is a stochastic process that depends on both the memory of each individual, called particle, and the knowledge gained by the population, called swarm. In the numerical implementation of this simplified social model, each particle has four attributes: the position vector in the search space, the velocity vector, the best position in its track and the best position of the swarm (Ma et al 2015). The process can be outlined as follows (Liu and Gong 2014):…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Hybrid Approach for Constrained Optimal Control of Nonlinear Switched Systems

Kahloul

Sakly

2020

J Control Autom Electr Syst

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This paper examines the problem of the optimal control of nonlinear switched systems under mixed constraints. The solution to this problem consists mainly in determining two major factors; on the one hand, we need to determine the optimal control input, and on the other one, we have to find out the best switching instants that lead to minimizing a particular functional cost. In order to reach this objective, we use a hybrid approach that consists in dividing the problem in two stages. In the first one, we employ the method of Lagrange multipliers while considering the Karush-Kuhn-Tucker (KKT) conditions. This method must be accompanied by the bang-bang control. This combination of these optimization methods enables us to obtain the optimal control input while respecting the simultaneously imposed constraints on the state and the input. In the second stage, a metaheuristic method is used to define the optimum switching instants, and this is what we call the particle swarm optimization (PSO). In order to approve this approach, different examples are applied in this study.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Hybrid Approach for Constrained Optimal Control of Nonlinear Switched Systems

Kahloul

Sakly

2020

J Control Autom Electr Syst

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“…A new transformed multi-innovation least squares (TMILS) algorithm it was used. Ma, J. et al in [7] proposes a new approach for identifying a Wiener-based model in which the system can be interpreted by an exogenous autoregressive model coupled with least squares and a support vector machine (LSSVM). The parameters were select by adaptive particle swarm optimization (APSO) that obtain better performance in relation of classical PSO.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO

Batista

Souza

Reis

et al. 2020

Sensors

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This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

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Identification of nonlinear Hammerstein system using mixed integer-real coded particle swarm optimization: application to the electric daily peak-load forecasting

Boubaker

2017

Nonlinear Dyn

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Identification of Wiener Model Using Least Squares Support Vector Machine Optimized by Adaptive Particle Swarm Optimization

Cited by 7 publications

References 36 publications

Hybrid Approach for Constrained Optimal Control of Nonlinear Switched Systems

Hybrid Approach for Constrained Optimal Control of Nonlinear Switched Systems

Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO

Identification of nonlinear Hammerstein system using mixed integer-real coded particle swarm optimization: application to the electric daily peak-load forecasting

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