Optimization of interval type-2 fuzzy system using the PSO technique for predictive problems

Mai, Dinh Sinh; Dang, Trong Hop; Ngo, Long Thanh

doi:10.1080/24751839.2020.1833141

Cited by 6 publications

(6 citation statements)

References 37 publications

(41 reference statements)

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“…In this case, optimal values of P, L, α 1 , α 2 , β1 , β2 , β3 , γ, R1 , R2 and R3 were computed considering three time-varying delays (n = 3) and wind speeds of 16 and 24 m/s, so that P = Q −1 , L = KP,β i −1 = βi , γ −1 = γ and R i −1 = Ri . According to the mentioned conditions, (40) and (20) were converted into the following forms:…”

Section: Simulationmentioning

confidence: 99%

“…Interval type-2 (T2) FLS, which includes membership functions (MFs) of fuzzy intervals, was proposed and proved to be more resistant to uncertainties than type-1 fuzzy logic. Interval T2FLS has many applications, the most recent of which includes fault detection [16,17], robotic control [18], medical diagnose [19], prediction problems [20], risk diagnosis [21] and financial investment [22]. The general T2FLS was introduced to improve IT2FLS performance in various applications.…”

Section: Introductionmentioning

confidence: 99%

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A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

et al. 2022

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This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system’s stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

et al. 2022

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“…Numerous methods such as the genetic algorithm [25], [26], neural networks [27], particle swarm optimization (PSO) [28], [29] harmony search algorithm [30], imperialist competitive algorithm [31], RCQEA [32]- [36], firefly algorithm and galactic swarm optimization [37], bee colony algorithm [38], slime mould algorithm [39], and shark smell optimization [40] have been used in IT2FLC optimization to improve its behaviour on the system.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

Şatır¹,

Baser²

2022

ELEKTRON ELEKTROTECH

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In recent years, quantum computing has gained immense popularity with the production of real quantum computers. Researchers have developed quantum-inspired evolutionary algorithms (QIEAs) to solve combinatorial optimization problems and have obtained successful results. As a special case of QIEAs, real-coded quantum evolutionary algorithm (RCQEA) is used in the optimization of high-dimensional complex problems. In this study, a novel mechanism of the quantum rotation gate (QRG) that is used to determine the rotation angle of the qubit in the RCQEA is introduced and implemented to accelerate the evolutionary process and increase the possibility of finding the optimal solution. Moreover, the skeleton of RCQEA is modified by using a clonal selection mechanism, and the real-coded quantum clonal selection algorithm (RCQCSA) is developed. Our proposed QRG accelerates the convergence speed of the algorithm. The main purpose of this study is to present a more effective algorithm that inspires quantum computing principles for optimizing the interval type-2 fuzzy logic controller (IT2FLC) membership functions (MFs). In this study, four different comparisons are made with these two different algorithms that have the original version of QRG and our proposed QRG. Optimized IT2FLC provides stabilization of the inverted pendulum system. The results show that the RCQCSA having our proposed QRG outperforms RCQEA in stabilizing the inverted pendulum system by optimizing the IT2FLC parameters.

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“…Fuzzy logic-based forecasting techniques have the advantage of working well on data where uncertainty exists and can give predictive results with high accuracy [ 14 ]. Therefore, many recent studies use fuzzy logic systems to solve forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

Mai

Bui

Doan

2023

Int. J. Fuzzy Syst.

Self Cite

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Forecasting the dam displacement is an important problem in dam monitoring. By using multi-temporal monitoring data to make predictions and forecast dam structure health, it is possible to detect abnormalities over time of the dam so that remedial measures can be taken promptly to minimize risks. The paper proposes a method of applying the interval type-2 fuzzy logic system (IT2FLS) and ant colony optimization (ACO) technique to forecast hydroelectric dams’ displacement. The method consists of two stages: The first is to design an IT2FLS according to the input and output data with base rules and newly constructed membership functions. The second is to find the optimal parameters for an interval type-2 fuzzy logic system based on the ACO technique and give the final fuzzy forecasting system. Experimental data are measured from hydroelectricity monitoring data of Ialy, Gia Lai Province, Vietnam, in the period from 2004 to 2016. The proposed method is compared with some other methods to compare prediction efficiency and accuracy. The experimental results show that the proposed model has the best performance compared to other standard methods in both accuracy and stability of prediction. This shows that a predictive model based on the IT2FLS is a promising method and can be used for forecasting time-series data. Supplementary Information The online version contains supplementary material available at 10.1007/s40815-022-01452-3.

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Optimization of interval type-2 fuzzy system using the PSO technique for predictive problems

Cited by 6 publications

References 37 publications

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

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