ANFIS-Based Compensation Algorithm for Current-Transformer Saturation Effects

Erenturk, Koksal

doi:10.1109/tpwrd.2008.2005882

Cited by 64 publications

(31 citation statements)

References 25 publications

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“…ANFIS, used as a valid predictor and identifier, has a similar functionality with fuzzy inference systems (FIS) [31]. Moreover, ANFIS has a number of advantages over FIS.…”

Section: Anfis (Adaptive Neuro-fuzzy Inference System)mentioning

confidence: 99%

Prediction of temperature distribution in human BEL exposed to 900 MHz mobile phone radiation using ANFIS

Develı

Sorgucu

2015

Applied Soft Computing

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“…ANFIS, used as a valid predictor and identifier, has a similar functionality with fuzzy inference systems (FIS) [31]. Moreover, ANFIS has a number of advantages over FIS.…”

Section: Anfis (Adaptive Neuro-fuzzy Inference System)mentioning

confidence: 99%

Prediction of temperature distribution in human BEL exposed to 900 MHz mobile phone radiation using ANFIS

Develı

Sorgucu

2015

Applied Soft Computing

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“…It can be described as a multilayered neural network as shown in Fig. 1, where the first layer executes a fuzzification process, the second layer executes the fuzzy AND of the antecedent part of the fuzzy rules, the third layer normalises the membership functions (MFs), the fourth layer executes the consequent part of the fuzzy rules and finally, the last layer computes the output of the fuzzy system by summing up the outputs of the fourth layer [31].…”

Section: Anfis Designmentioning

confidence: 99%

Development of an adaptive neural‐fuzzy inference system based meta‐model for estimating lightning related failures in polluted environments

Shariatinasab

Safar

Mobarakeh

2014

IET Science, Measurement & Technology

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Owing to the random nature of the lightning phenomenon, the statistical based methods such as Monte Carlo simulation are the best tool to perform the lightning-related studies. However, Monte Carlo simulation is complex and time consuming, especially, for larger networks where a lot of regions must be investigated, separately. Although some researches have been carried out to evaluate the lightning flashover outage rates based on artificial intelligence (AI) techniques, but, so far, any work has not been reported to perform a risk assessment of lightning surges by means of AI. This study presents a meta-model based on adaptive neural-fuzzy inference system (ANFIS) that can be utilised to perform the lightning-related risk analysis with a good accuracy. The inputs of ANFIS are tower footing resistance, critical flashover voltage and the rate of lightning occurrence; and its outputs include the failure risk of both the insulation and arrester. The trained ANFIS can be very efficient in terms of time and computational resources for the further analysis, like placement of arresters or insulation coordination studies, on the electrical network on which the training is performed. To demonstrate the effectiveness of the ANFIS meta-model, an optimisation method, based on genetic algorithm, has been implemented in order to find the optimum location of a specified set of line arresters on power network. Also, in order to consider the environmental condition, that is, pollution and altitude, a new method to determine the importance of each node of interest in protection is introduced.

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“…Fuzzy modeling on the other hand provides another alternative for model construction. Adaptive Neuro Fuzzy Inference System (ANFIS) has shown the ability in estimating nonlinear system for different applications [10][11][12][13][14]. Comparison between ARX model and ANFIS model has shown ANFIS model exhibits significantly more accurate model [9,15].…”

Section: Introductionmentioning

confidence: 99%

ANFIS modeling and Direct ANFIS Inverse control of an Electro-Hydraulic Actuator system

Ling

Rahmat

Husain

2013

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

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The existence of high degree of nonlinearity in Electro-Hydraulic Actuator (EHA) has imposed a challenging work in developing a representable model for the system and controller design such that significant control performance can be achieved. The objectives of this paper are to generate an accurate EHA model using ANFIS approach and obtain a controller using Direct ANFIS Inverse approach. The ANFIS model is able to represent the nonlinear EHA system at high accuracy and low Root Mean Squared Error (RMSE). Direct ANFIS inverse controller has shown the ability in position tracking of EHA system in step and sine response. Besides, ANFIS controller is able to handle external disturbances to the system.

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ANFIS-Based Compensation Algorithm for Current-Transformer Saturation Effects

Cited by 64 publications

References 25 publications

Prediction of temperature distribution in human BEL exposed to 900 MHz mobile phone radiation using ANFIS

Prediction of temperature distribution in human BEL exposed to 900 MHz mobile phone radiation using ANFIS

Development of an adaptive neural‐fuzzy inference system based meta‐model for estimating lightning related failures in polluted environments

ANFIS modeling and Direct ANFIS Inverse control of an Electro-Hydraulic Actuator system

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