Volterra Series identification Based on State Transition Algorithm with Orthogonal Transformation

Zhang, Hongli; Fan, Wenhui

doi:10.12928/telkomnika.v14i1.2663

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Dong et al (2016) proposed the concept of quadratic state transition to solve the assignment problems, which expanded the scope of candidate solutions and increased the diversity of candidate solutions. In (Wang et al, 2016), a quantum state transition algorithm is used to solve the job shop scheduling problems. It combines the shift decoding and position exchange coding to map the solution space, and the non-local optimal solution tolerance mechanism is proposed to improve the convergence accuracy.…”

Section: Introductionmentioning

confidence: 99%

A hybrid discrete state transition algorithm for combinatorial optimization problems

Jiang

Shen

2023

Front. Energy Res.

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The discrete state transition algorithm (DSTA) has been wildly applied to deal with combinatorial optimization problems. However, its low convergence accuracy limits its application in large-scale optimization problems. Aiming at the convergence performance and search intensity of the algorithm, a hybrid discrete state transition algorithm (HDSTA) is proposed in this work by introducing tabu search and elite solution set. Firstly, a searching mechanism with the integration of DSTA and tabu search (TS) is established, which allows moving to adjacent solutions at an increased cost to escape from the local optimum. Specifically, a tabu list as adaptive memory is adopted to avoid the loop when deviating from local optima. Secondly, an elite solution set is introduced to integrate the information of the previous optimal solution and the global optimal solution, and the search strategy is modified to expand the range and diversity of candidate solutions. Finally, the proposed HDSTA is verified according to the real data on two well-known optimization problems (staff assignment problem and traveling salesman problem) and the real data of an industrial case. The experimental results show the effectiveness of the proposed algorithm in large-scale optimization problems.

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Section: Introductionmentioning

confidence: 99%

A hybrid discrete state transition algorithm for combinatorial optimization problems

Jiang

Shen

2023

Front. Energy Res.

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“…20 Wang et al developed a Volterra kernel identification method based on state transition algorithm with orthogonal transformation. 21 Although these methods have good identification results, they are not suitable for models with high orders. The length of the Volterra system increases exponentially with the increase of the memory length and order, it leads to the curse of dimensionality.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Bouvier et al proposed an order separation method for a nonlinear homogeneous model, where the model is approximated by a Volterra model 20 . Wang et al developed a Volterra kernel identification method based on state transition algorithm with orthogonal transformation 21 . Although these methods have good identification results, they are not suitable for models with high orders.…”

Section: Introductionmentioning

confidence: 99%

Kernel‐based regularization least squares algorithm for nonlinear time‐delayed systems using self‐organizing maps

Zhang

Chen

et al. 2023

Intl J Robust & Nonlinear

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This article proposes a kernel-based regularization least squares algorithm for nonlinear time-delayed systems with unknown structure. Since the structure and the time-delay of the model are unknown, a model pool constituted of several Volterra series is constructed with the aim of approximating the nonlinear model which has different time-delays. Then, a self-organizing maps method and a kernel-based regularization method are interactively used to update the time-delay and parameters. Compared with the traditional algorithms, the proposed algorithm has no limitation on the nonlinear model, and can describe the dynamics of the nonlinear model with a simple structure. Finally, simulation examples are given to show the effectiveness of the algorithm.

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Volterra Series identification Based on State Transition Algorithm with Orthogonal Transformation

Abstract: Abstract

Cited by 2 publications

References 11 publications

A hybrid discrete state transition algorithm for combinatorial optimization problems

A hybrid discrete state transition algorithm for combinatorial optimization problems

Kernel‐based regularization least squares algorithm for nonlinear time‐delayed systems using self‐organizing maps

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