Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization

Ahmed, Troudi; Mohamed, Bouzbida; Châari, Abdelkader

doi:10.1142/s0218488515500294

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…6. Calculate the estimated output ĥ1 (k, t) by equation (40), and compute the estimated noise n(k, t) by equation (41). Form the estimated stacked noise vector V(k, m) by equation ( 42).…”

Section: Hml Identificationmentioning

confidence: 99%

“…The PSO algorithm has extensive applications in system identification. For example, Ahmed et al 40 derived the new clustering algorithm based on PSO and applied it to nonlinear parameter estimation; Zheng and Liao 41 combined social emotional models and PSO to identify parameters in nonlinear systems. Here, we use the PSO to estimate nonlinear parameters in subsystem after decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

Zong

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article covers the identification of the feedback nonlinear systems. The parameterized nonlinear identification model involves products of system nonlinear part parameters and system linear part parameters. To deal with these product terms, the hierarchical identification based on maximum likelihood is used to decompose the system into two subsystems. The Levenberg–Marquardt and particle swarm optimization methods are applied for identifying the two subsystems, respectively. For the purpose of enhancing the optimization capability of the basic particle swarm optimization method, some improvement strategies are constructed. Thus, the improved particle swarm optimization algorithm which combines the adaptive feedback, the linearly decreasing method and the mutational operator is applied to adaptively adjust the search strategy and improve the ergodicity. The simulation indicates that the derived estimation is effective because it can obtain high identification accuracy and fast convergence speed when identifying the feedback systems. Besides, the case study demonstrates that the proposed method can be well used to identify the electro-hydraulic-servo position system.

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“…6. Calculate the estimated output ĥ1 (k, t) by equation (40), and compute the estimated noise n(k, t) by equation (41). Form the estimated stacked noise vector V(k, m) by equation ( 42).…”

Section: Hml Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

Zong

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…If this phenomenon cannot be effectively curbed, it will bring huge disasters to the entire country and the whole society 3 . Therefore, cybersecurity has become one of the most concerned issues in the world 4 …”

Section: Introductionmentioning

confidence: 99%

Internet of things network intrusion detection model based on quantum artificial fish group and fuzzy kernel clustering algorithm

Zhang

2022

Security and Privacy

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The application of internet of things (IoT) devices in 5G/B5G has benefited many industries, such as automated production, smart agriculture and autopilot, and has provided access to billions of smart devices. However, the rapid growth of IoT devices has also brought network security issues. The purpose of this paper is to study the network intrusion detection model based on quantum artificial fish population and fuzzy kernel clustering algorithm. In this paper, the network intrusion detection model based on traditional fuzzy C‐means (FCM) clustering has poor classification effect and is prone to local extremum. A semi‐supervised fuzzy kernel–clustering algorithm based on quantum artificial fish group is proposed. The algorithm adopts a small amount of tag data and many unknown tag data to generate the classification of network intrusion detection and constructs a new objective function of FCM algorithm by means of kernel distance. In the simulation experiment of KDD Cup 99 network intrusion detection data, compared with the intrusion detection model based on FCM, particle swarm optimization‐FCM, the detection rate of the proposed algorithm in the known attack and unknown attack is 92.5% and 84.9%, respectively. The results show that the proposed intrusion detection algorithm has better detection rate.

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A nonlinear Wiener system identification based on improved adaptive step-size glowworm swarm optimization algorithm for three-dimensional elliptical vibration cutting

Wang

et al. 2019

Int J Adv Manuf Technol

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Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization

Cited by 5 publications

References 13 publications

Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

Internet of things network intrusion detection model based on quantum artificial fish group and fuzzy kernel clustering algorithm

A nonlinear Wiener system identification based on improved adaptive step-size glowworm swarm optimization algorithm for three-dimensional elliptical vibration cutting

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