Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system

Danesh, Sedigheh; Farnoosh, Rahman; Razzaghnia, Tahereh

doi:10.1016/j.neucom.2015.09.019

Cited by 18 publications

(9 citation statements)

References 27 publications

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“…In fuzzy segment, only zero or primary order Sugeno inference scheme or Tsukamoto presumption scheme can be utilized. This segment initiates the fundamentals of ANFIS network design and its hybrid learning regulation [38]. The Sugeno fuzzy model was proposed by Takagi, Sugeno, and Kang in an effort to formalize a systematic approach to generating fuzzy rules from an input-output dataset.…”

Section: Architecture Of Anfismentioning

confidence: 99%

Optimal Power Dispatch of WECS and UPFC with ACO and ANFIS Algorithms

Vig¹,

Surjan²

2018

IJEEI

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Wind energy conversion systems are the quickest developing renewable source of electrical energy having tremendous environmental and social advantages. In this paper proposed an optimal reactive power dispatch (ORPD) technique of wind power plants in grid connected power system. The ORPD issue is a vital issue in the operation of power frameworks. It is a nonlinear and mixed integer programming issue, which decides ideal qualities for control parameters of reactive power makers to upgrade particular target capacities while fulfilling a few specialized requirements. The ORPD technique consists of ACO and ANFIS controller for improving the performance of multiple wind turbines and the steady state stability of the power system in terms of active and reactive power flow of a transmission line. The ACO algorithm is used to extract the maximum power from WECS. The generated maximum power is transmitted through the UPFC connected HVDC link. The performance of the UPFC is improved with the ANFIS controller based on the actual and the reference power parameters of the grid connected power system. Furthermore, UPFC is utilized to control active and reactive flow of power in a transmission line. The proposed method is implemented in MATLAB/Simulink platform and tested with the different wind speed condition. To verify the effectiveness of the proposed model, the obtained results in the deterministic case is compared with the existing methods like GA and PSO.

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Section: Architecture Of Anfismentioning

confidence: 99%

Optimal Power Dispatch of WECS and UPFC with ACO and ANFIS Algorithms

Vig¹,

Surjan²

2018

IJEEI

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“…Fuzzy neural networks have been applied for the fuzzy regression (see, e.g. [7,8,10,18,23,25]). Jang [21] proposed the adaptive networkbased fuzzy inference system (ANFIS) in 1993 and Cheng and Lee [3] established the ANFIS model for fuzzy regression analysis using linear programming, and studied on both fuzzy adaptive networks and the switching regression model in 1999.…”

Section: Introductionmentioning

confidence: 99%

“…In a study of Takagi and Sugeno [33], the method was presented for identifying a system using its input-output data. Also in 2009 and 2014, Dalkilic and Apaydin [7,8] used the ANFIS model to analyze switching regression and estimate the fuzzy regression parameters, and in 2016, Danesh et al [9,10] used the ANFIS model to predict fuzzy regression model. Generally for real-world applications, data sets often contain multiple variables as well as noise or outliers that are inconsistent with the other data.…”

Section: Introductionmentioning

confidence: 99%

Regression parameters prediction in data set with outliers using neural network

Razzaghnia¹

2019

Hacettepe Journal of Mathematics and Statistics

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Popular regression techniques often suffer at the presence of data outliers. The different methods have proposed to make smaller the effect of the outlier on the parameter estimates. In this study, an algorithm has been addressed based on Adaptive network based fuzzy inference system to define the unknown parameters of regression model where dependent variable has outlier. So, three numerical examples are solved to test the activity of the proposed algorithm in regression model estimation. Also, the obtained results from the different methods, such as linear programming (LP) and fuzzy weights with linear programming (FWLP) are compared together. The results show that the proposed method is not to be affected the outliers in the solving process.

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“…The time-series models are introduced in [16]. The adaptive neuro-fuzzy interface models (ANFIS) are reported in [17][18] respectively. Various regression modes are investigated in [19].…”

Section: Introductionmentioning

confidence: 99%