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

Tavoosi, Jafar; Mohammadzadeh, Ardashir; Jermsittiparsert, Kittisak

doi:10.1007/s00500-021-05686-5

Cited by 27 publications

(9 citation statements)

References 87 publications

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“…A more complex approach is represented by IT2FS, where the concept of uncertainty in the form of intervals is introduced. Although computationally complex compared to T1FS, they derive an improvement in the general fuzzy model by being more resistant to external noise, as reported by Castro et al [2]; Puška et al [3]; Eren [4]; Tavossi et al [5].…”

Section: Introductionmentioning

confidence: 95%

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Janková

Dostál

2021

Mathematical Problems in Engineering

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Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

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Section: Introductionmentioning

confidence: 95%

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Janková

Dostál

2021

Mathematical Problems in Engineering

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“…The uncertainties are represented by a fuzzy MF. As a result, T2FLSs are better suited to situations where determining the precise MF for a fuzzy set (FS) is challenging, which is particularly beneficial for incorporating uncertainties (Mendel 2017, Tavoosi, Mohammadzadeh et al 2021. Type-1 FS (T1FS)s have completely certain MFs, whereas type-2 FS (T2FS)s have fuzzy MFs.…”

Section: Different Types Of Flssmentioning

confidence: 99%

Survey on the Recent Design and Tuning Approaches for model-free Fuzzy PID/PI Controllers

Sabahi

Mohammadzadeh

Tavan

et al. 2022

Preprint

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There is good experience in applying classical linear proportional, integral and derivative (PID) controllers to industrial plants due to its simple structure and easy tuning property. However, considering the fact that these linear controllers have fixed parameters, the uncertainties and nonlinearities in the controlled system can degrade the control performance. To tackle the mentioned problems in the application of PID controllers, one of the ways is to incorporate fuzzy logic systems (FLSs) into the PIDs and fine-adjust them. Like the PID controllers, one of the advantages of the FLS-based PID controllers is that they do not require a system mathematical model for control problems, thus these controllers have shown to be a viable control solution for various complicated nonlinear systems. This study presents a survey of development and analysis of model-free PIDs incorporated with FLSs. We focus especially on the widely used fuzzy gain scheduling PID (FGPID) and fuzzy PID (FPID) controllers which are two important combinations of the FLSs and PID controllers. In this study, different structures of FGPID and FPID controllers in which type-1 FLS (T1FLS), interval type-2 FLS (IT2FLS), and general type-2 FLS (GT2FLS) are used as the inference realization part, have also been reviewed. Moreover, the tuning methods of FGPID and FPID controllers’ parameters and their closed-loop stability problems are discussed.

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“…In the design of transmission lines, a constant value is determined as the line capacity to limit the power passing through it, while if the cooling conditions of the conductor such as wind speed and air temperature are appropriate, it can pass more power. It should be noted that due to the benefits of artificial intelligence, this technology can also be used to predict the capacity of transmission lines Tavoosi et al (2021a), Tavoosi et al (2021b). In order to solve the problem of transmission line capacity, we can use its emergency capacity, but in this case, due to the passage of additional power through the transmission lines, the temperature of these lines rises and causes damages such as loss of elastic thickness in the line Palm (2021); Pandey et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Price of Emergency Power Transmission Lines in the Reserve Market Due to the Influence of Renewable Energies

et al. 2022

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The law of free access to the transmission network obliges the transmission network to be in orbit, and on the other hand, the high loads in the transmission network, and economic uncertainties cause that the owners of transmission companies, don’t have sufficient motivation and resources to rebuild and develop the network. The main objective of this paper is the modeling the price of emergency power transmission lines in the reserve markets. This paper presents a method for calculating the reference price that a transmission line owner uses to bid on a price in excess of the nominal capacity of the transmission line under his ownership. For this purpose, first, the effects of operating a transmission line at a power greater than the rated power are described. After that, the reduction rate of the transmission line due to operation in these conditions is calculated, and finally the price determination is calculated based on the reduction rate of the generated life. In the next stage, this excess capacity is entered the two-stage model of energy market and reservation considering renewable energy sources as a price offer function. Numerical results of 6-Shin network show that the entry of renewable energy sources reduces energy costs, but the costs of the reserve market increases due to uncertainty. However, despite the emergency capacity, these costs are reduced due to the use of cheap resources in the network.

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

Cited by 27 publications

References 87 publications

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Survey on the Recent Design and Tuning Approaches for model-free Fuzzy PID/PI Controllers

Modeling the Price of Emergency Power Transmission Lines in the Reserve Market Due to the Influence of Renewable Energies

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