WH-MOEA: A Multi-Objective Evolutionary Algorithm for Wiener-Hammerstein System Identification. A Novel Approach for Trade-Off Analysis Between Complexity and Accuracy

Zambrano, J.; Sanchis, Javier; Herrero, J. M.; Martinez, Miren Itsaso

doi:10.1109/access.2020.3046352

Cited by 7 publications

(3 citation statements)

References 56 publications

(75 reference statements)

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“…Since the target system is SIMO with two outputs, two output models to describe the dynamics of MWO and MSP are developed independently. The discrete prediction equation is used as a prediction model of each output as follows [21],…”

Section: Development Of the Discrete Prediction Modelmentioning

confidence: 99%

Supplementary Control of Conventional Coordinated Control for 1000 MW Ultra-Supercritical Thermal Power Plant Using One-Step Ahead Control

Choi,

Moon

et al. 2023

Energies

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The intermittence of renewable energy sources increases the importance of the effective load-tracking ability of power plants. Coordinated control between boiler and turbine systems is the uppermost layer of a thermal power plant control to follow the load demand. In this paper, a supplementary controller is proposed based on the One-Step Ahead strategy for coordinated control of thermal power plants. After a plant model is developed offline from a step response test, the optimized control of the One-Step Ahead strategy is applied to the boiler feed-forward (BFF) signal to control the electric power output and the main steam pressure simultaneously. Simulation with a 1000 MW ultra-supercritical (USC) once-through type power plant is performed. The results show that the error of Mega-Watt Output (MWO) was reduced to 78~95%, and settling time was reduced to 64~79% from conventional coordinated control by adding the proposed supplementary controller.

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Section: Development Of the Discrete Prediction Modelmentioning

confidence: 99%

Supplementary Control of Conventional Coordinated Control for 1000 MW Ultra-Supercritical Thermal Power Plant Using One-Step Ahead Control

Choi,

Moon

et al. 2023

Energies

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“…Анализ компромисса между точностью и сложностью уже использовался для идентификации моделей на основе обработки набора измерений характеристик моделируемого явления и при идентификации моделей Винера - Гаммерштейна [12]. Многокритериальный генетический алгоритм, который позволяет несколькими критериями влиять на выбор структур -кандидатов модели, применяется при идентификации полиномиальных моделей для реальной нелинейной системы и позволяет лучше выбирать окончательную идентифицированную модель [13].…”

Section: Introductionunclassified

Analysis of the trade-off between accuracy and complexity of identified models of dynamic systems

Zhang

2024

Analysis and data processing systems

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One of the rapidly developing areas of the modern control theory is the identification of systems associated with the construction of mathematical models of systems in the form of a set of mathematical relationships that adequately reflect the basic properties of the system. Symbolic regression methods are becoming more and more popular in the problems of structural-parametric identification of systems based on available observations and experimental data, which allow building regression models in the form of codes of mathematical expressions in a symbolic form. Among the known numerical evolutionary methods of symbolic regression, the universally acknowledged “favorite” is the method of genetic programming”, the application of which allows us to describe the search for solving a problem as the construction of a regression model by enumerating various arbitrary superpositions of functions from some predetermined set. In this case, the important indicators determining the quality of identification of the mathematical model of the system are the accuracy and complexity of the identified model. Often, the system model obtained as a result of solving identification problem is not accurate enough or excessively complex. As a result, the solution to the identification problem is inextricably linked to ensuring sufficient accuracy and simplicity of the identified model. In this connection, it is natural to adhere to the principle of balanced identification, which indicates the search for a compromise between the accuracy of reproduction and the measure of complexity of the identified model. The purpose of this paper, which develops the concept of balanced identification, is to analyze the trade-off between the accuracy and complexity of models of dynamic systems identified by genetic programming. In this paper we introduce a functional "accuracy-complexity", which allows us to balance the trade-off between these key indicators of the identified models when solving the identification problem. The effectiveness of the proposed functional is demonstrated on the example of computer identification by genetic programming of a Lorentz dynamical system.

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“…Pareto fronts of each design concept allows a more in-depth analysis to select the most appropriate model according to the user's needs. As in the previous chapters, this chapter is a literal transcription with minor modifications of the following research publication[120]:…”

mentioning

confidence: 99%

Identification of nonlinear processes based on Wiener-Hammerstein models and heuristic optimization.

Abad¹,

César²

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WH-MOEA: A Multi-Objective Evolutionary Algorithm for Wiener-Hammerstein System Identification. A Novel Approach for Trade-Off Analysis Between Complexity and Accuracy

Cited by 7 publications

References 56 publications

Supplementary Control of Conventional Coordinated Control for 1000 MW Ultra-Supercritical Thermal Power Plant Using One-Step Ahead Control

Supplementary Control of Conventional Coordinated Control for 1000 MW Ultra-Supercritical Thermal Power Plant Using One-Step Ahead Control

Analysis of the trade-off between accuracy and complexity of identified models of dynamic systems

Identification of nonlinear processes based on Wiener-Hammerstein models and heuristic optimization.

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