Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN

Tavoosi, Jafar; Suratgar, Amir Abolfazl; Menhaj, Mohammad Bagher

doi:10.1016/j.engappai.2016.04.006

Cited by 38 publications

(14 citation statements)

References 33 publications

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“…In their proposed network, the long short-term memory mechanism has been used to recurrent structure, which avoids the gradient vanishing problem that there is in the classic recurrent fuzzy neural systems and improves the performances for sequential data with long-time dependency feature. A recurrent interval TSK T2F-NN has been used for trajectory tracking problem of an experimental mobile robot (Tavoosi 2016 ). The mentioned paper uses a simple T2F-NN structure and its innovation is hardware implementation on a robot, but unfortunately there is no discussion about how to implement and real time operation of the system.…”

Section: T2f-nnsmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

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Section: T2f-nnsmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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“…Fuzzy cmeans clustering and high order cognitive map were exerted by Lu in order to model and predict time series on the basis of type-1 fuzzy sets [16]. T2FS identification has engrossed so many researchers [17][18][19][20][21][22][23]. Abiyev et al [17] took advantage of T2F clustering to organize construction of a wavelet type-2 TSK fuzzy neural system.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

Tavoosi¹,

Suratgar²,

Menhaj³

et al. 2021

Preprint

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Not only does this paper present a novel type-2 fuzzy system for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also it brings forward an exquisite technique to acquire an optimal number of membership functions and the corresponding rules. It proposes a seven-layered NCPRT2FS. For fuzzification in the first two layers, Gaussian type-2 fuzzy membership functions with uncertainty in the mean, are exploited. The third layer comprises rule definition and the forth one embeds fulfillment of type reduction. The three last remained layers are the ones in which resultant left–right firing points, two end-points and output all get assessed correspondingly. It should not be neglected off the nutshell that recurrent feedback at the fifth layer exerts delayed outputs ameliorating efficiency of the suggested NCPRT2FS. Later in the paper, a modern structural learning, established on type-2 fuzzy clustering, is held forth. An adaptively rated learning back-propagation algorithm is extended to adjust the parameters ensuring the convergence as well. Eventually, solar cell photo-voltaic and wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge so persuasive.

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“…Computational intelligence tools in complex systems have performed well in terms of modeling and system identification [10][11][12][13], control and regulation [14][15][16][17], and so on [18][19][20]. Neural networks, fuzzy logic, and evolutionary algorithms have been very efficient in combining model-based methods (control theory) in aerospace systems [21,22].…”

Section: Introductionmentioning

confidence: 99%

A New Type-2 Fuzzy Sliding Mode Control for Longitudinal Aerodynamic Parameters of a Commercial Aircraft

Tavoosi¹

2020

JESA

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Although sliding mode control has many advantages such as stability and robustness but there are two important disadvantages as follow: Chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. So, this paper presents a new method of adaptive sliding mode control based on general type-2 fuzzy logic to overcome on the mentioned problems. First, the longitudinal motion equations of a commercial aircraft and the upper limits of the unknown functions are introduced, which include the driving errors and uncertain parameters of the model. Then, a general type-2 fuzzy neural network (GT2FNNs), with adaptive rules, estimates these limits. Estimating the limits can reduce the computational load with less rules and weight than the dynamic matrix. The Boeing 747 is being studied and an attempt has been made to use a model very close to this aircraft. The stability of the control system has been proven. The simulation results show that by applying three models of faults to the aircraft system, the proposed type-2 fuzzy-based sliding mode control has excellent performance, especially in controlling the Aileron and Rudder angles.

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Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN

Cited by 38 publications

References 33 publications

A review on type-2 fuzzy neural networks for system identification

A review on type-2 fuzzy neural networks for system identification

Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

A New Type-2 Fuzzy Sliding Mode Control for Longitudinal Aerodynamic Parameters of a Commercial Aircraft

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