Forecasting by TSK general type‐2 fuzzy logic systems optimized with genetic algorithms

Chen, Yang; Wang, Dazhi; Wu, Ning

doi:10.1002/oca.2353

Cited by 30 publications

(23 citation statements)

References 42 publications

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“…There are still many interesting works that lie ahead, including studying the center of sets TR of IT2 and general T2 FLSs [30,31,[34][35][36][37]45] and investigating forecasting and control problems based on IT2 and GT2 FLSs optimized with swarm intelligence algorithms [46][47][48][49][50][51]. Future studies will give attention to the T2 FLSs design and applications.…”

Section: Discussionmentioning

confidence: 99%

Study on Centroid Type‐Reduction of Interval Type‐2 Fuzzy Logic Systems Based on Noniterative Algorithms

Chen

2019

Complexity

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Interval type-2 fuzzy logic systems have favorable abilities to cope with uncertainties in many applications. While the block type-reduction under the guidance of inference plays the central role in the systems, Karnik-Mendel (KM) iterative algorithms are standard algorithms to perform the type-reduction; however, the high computational cost of type-reduction process may hinder them from real applications. The comparison between the KM algorithms and other alternative algorithms is still an open problem. This paper introduces the related theory of interval type-2 fuzzy sets and discusses the blocks of fuzzy reasoning, type-reduction, and defuzzification of interval type-2 fuzzy logic systems by combining the Nagar-Bardini (NB) and Nie-Tan (NT) noniterative algorithms for solving the centroids of output interval type-2 fuzzy sets. Moreover, the continuous version of NT (CNT) algorithms is proved to be accurate algorithms for performing the type-reduction. Four computer simulation examples are provided to illustrate and analyze the performances of two kinds of noniterative algorithms. The NB and NT algorithms are superior to the KM algorithms on both calculation accuracy and time, which afford the potential application value for designer and adopters of type-2 fuzzy logic systems.

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Section: Discussionmentioning

confidence: 99%

Study on Centroid Type‐Reduction of Interval Type‐2 Fuzzy Logic Systems Based on Noniterative Algorithms

Chen

2019

Complexity

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“…Simulation results show that the proposed Mamdani GT2 FLSs optimized with QPSO algorithms have better performances than Mamdani T1 and IT2 counterparts. It is grounded to develop more generalized T2 FLSs optimized with evolutionary algorithms (Castillo and Melin, 2013; Chen et al, 2018; Das et al, 2015; Hidalgo et al, 2012; Hsu and Juang, 2013; Lu, 2015; Zhai et al, 2012) for real applications. However, we need to choose the initial parameters of FLSs smartly to make them converge much faster.…”

Section: Discussionmentioning

confidence: 99%

“…Here, we select the non-singleton fuzzifier and center-of-set (COS) type-reduction (Chen et al, 2016a). Firstly, the firing interval F α s ( x ) for each of the fuzzy rules at the specific α -level is calculated as…”

Section: Mamdani Gt2 Flssmentioning

confidence: 99%

“…The particles searched by QPSO algorithm can be found in the whole feasible solution space. Therefore, QPSO algorithm (Chen et al, 2016a; Fan et al, 2018; Sun et al, 2012) is globally convergent searching iteration algorithm that is different from the derivative-based locally iteration algorithm (Chen et al, 2016b; Wang and Chen, 2018). It is very difficult to calculate derivatives in IT2 FLSs (Mendel, 2014), let alone in GT2 FLSs.…”

Section: Qpso Algorithms and Mamdani Gt2 Flss Optimizationmentioning

confidence: 99%

“…Here, we define the following three evaluation indices to test the performances of FLSs forecasters. They are mean absolute percentage error (MAPE) (Khosravi and Nahavandi, 2014), root mean square error (RMSE) (Chen et al, 2016b; Wang and Chen, 2018), and comprehensive evaluation error sum (CEES) (Chen et al, 2016a), respectively.…”

Section: Simulationsmentioning

confidence: 99%

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Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms

Chen

Wang

2019

Transactions of the Institute of Measurement and Control

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Much more attention has been focused on studying and applying general type-2 fuzzy logic systems (GT2 FLSs) in recent years. The paper designs a type of Mamdani GT2 FLS for studying forecasting problems based on the data of permanent magnetic drive (PMD) loss. During the system design process, we choose the primary membership functions (MFs) of antecedent, consequent and input measurement general type-2 fuzzy sets (GT2 FSs) as Gaussian type MFs with uncertain standard deviations. The corresponding vertical slices (secondary MFs) are chosen as the triangle MFs. All the parameters of Mamdani GT2 FLSs are optimized by the quantum particle swarm optimization (QPSO) algorithms. Noisy data of PMD loss are adopted for both training and testing the proposed FLSs forecasting approaches. Simulation studies and convergence analysis are employed to show the effectiveness and feasibility of the proposed GT2 FLSs forecasting methods compared with their T1 and IT2 counterparts.

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Nash game‐based adaptive robust control design optimization for the underactuated mechanical system with fuzzy evidence theory

Zheng

Zhao

2021

Optim Control Appl Methods

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This article proposes a novel Nash game‐theoretical optimal adaptive robust control design approach to address the constraint‐following control problem for the uncertain underactuated mechanical systems with fuzzy evidence theory. First, the uncertainty is considered bounded and the bound is unknown but lies in a specified fuzzy evidence number. Second, a deterministic adaptive robust control scheme is proposed based on the servo constraint following control method, which renders the uncertain underactuated mechanical system to follow the specified constraints accurately with deterministic performance (guarantee uniform boundedness and uniform ultimate boundedness). It is shown that the designed self‐adjusting leakage‐type adaptive law can compensate for the uncertainty and avoid overcompensation. Third, based on the performance analysis and the fuzzy evidence description of uncertainty, the Nash game theory is introduced into the multi‐parameter optimization design for the two tunable control gains selected as two players. The cost functions for two players are relevant to the system constraint‐following performance and control cost. Then we can obtain the optimal control gains by seeking the Nash equilibrium which is always proved to exist. Ultimately, the simulation results on the two‐wheeled self‐balancing robot demonstrate the availability of the proposed control scheme and the optimal design approach for the underactuated mechanical systems with uncertainties.

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Forecasting by TSK general type‐2 fuzzy logic systems optimized with genetic algorithms

Cited by 30 publications

References 42 publications

Study on Centroid Type‐Reduction of Interval Type‐2 Fuzzy Logic Systems Based on Noniterative Algorithms

Study on Centroid Type‐Reduction of Interval Type‐2 Fuzzy Logic Systems Based on Noniterative Algorithms

Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms

Nash game‐based adaptive robust control design optimization for the underactuated mechanical system with fuzzy evidence theory

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