Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Abdulshahed, Ali; Longstaff, Andrew P.; Fletcher, Simon; Myers, Alan

doi:10.1016/j.apm.2014.10.016

Cited by 142 publications

(65 citation statements)

References 16 publications

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“…It combines the advantages of a fuzzy controller as well as quick response and adaptability nature of artificial neural network [11]. Hence using this scheme one can avoid the use of a position sensor which produces mechanical losses in the motor.…”

Section: Anfis Estimatormentioning

confidence: 99%

“…In order to explain the concept of ANFIS structure, five distinct layers are used. The first layer in the ANFIS structure is the fuzzification layer; the second layer performs the rule base layer; the third layer performs the normalisation of membership functions (MFs); the fourth and fifth layers are the defuzzification and summation layer respectively [11]. This rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of ANFIS Estimator of Permanent Magnet Brushless DC Motor Position for PV Pumping System

Terki¹,

Moussi²,

Terki³

2015

ijacsa

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Abstract-This paper presents a new scheme for PMBLDC (permanent magnet brushless direct current) rotor position estimation based an ANFIS (adaptive network fuzzy inference system) estimator. The operation of such motor requires accurate rotor position knowledge. However, most of rotor position sensors produce undesirable effects such as mechanical losses and have other disadvantages. In order to overcome the disadvantages, sensorless scheme seems to offer great advantages. This work present an ANFIS estimator design. Combining the adaptive capability of the neural network to gather with the reasoning ability of the fuzzy logic in ANFIS modeling results in a fast responding and flexible model. This procedure lends itself perfectly adapted for complex system such as PV pumping systems.

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Section: Anfis Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of ANFIS Estimator of Permanent Magnet Brushless DC Motor Position for PV Pumping System

Terki¹,

Moussi²,

Terki³

2015

ijacsa

View full text Add to dashboard Cite

show abstract

“…There are two learning strategies in neural part of the ANFIS: Hybrid learning and back-propagation learning methodologies. In fuzzy part of the ANFIS, only first or zero order Sugeno inference system may be used [23,24]. The architecture of fuzzy section of the developed ANFIS for 4-CP removal is shown in Figure 1(b).…”

Section: Anfis-fcm Modelingmentioning

confidence: 99%

“…The system is an intelligent hybrid method, which uses features of the Takagi-Sugeno fuzzy inference system and adaptive neural network [23]. An ANFIS model is usually defined via five certain layers for the purpose of description the model structure (Figure 1(a)).…”

Section: Anfismentioning

confidence: 99%

Application of Adaptive Neural Fuzzy Inference System and Fuzzy C- Means Algorithm in Simulating the 4-Chlorophenol Elimination from Aqueous Solutions by Persulfate/Nano Zero Valent Iron Process

Baziar¹,

Nabizadeh²,

Mahvi³

et al. 2017

Eurasian J Anal Chem

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This study investigated the application of adaptive neural fuzzy inference system (ANFIS) and Fuzzy c-means (FCM) algorithm for the simulation and prediction of 4-chlorophenol elimination in aqueous media by the persulfate/Nano zero valent iron process. The structure of developed model which resulted to the minimum value of mean square error was a Gaussian membership function with a total number 10 at input layer, a linear membership function at output layer and a hybrid optimum method, which is a combination of backpropagation algorithm and least squares estimation, for optimization of Gaussian membership function parameters. The prediction of developed model in elimination 4-chlorophenol was significantly close to the observed experimental results with R2 value of 0.9942. The results of sensitivity analysis indicated that all operating variables had a strong effect on the output of model (4-CP elimination). However, the most effective variable was pH followed by persulfate, NZVI dosage, reaction time and 4-CP concentration. The performance of developed model was also compared with a quadratic model generated in a study by Response Surface Methodology (RSM). The results indicated that the ANFIS-FCM model was superior to the quadratic model in terms of prediction accuracy and capturing the behavior of the process.

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“…In general, the fuzzy C-means algorithm (FCM) [7][8][9][10][11][12] is more effective.FCM allows pixels to have relation with multiple clusters with varying degrees of membership. The conventional FCM algorithm is quite difficult to deal with patterns with different volumes.…”

Section: Introductionmentioning

confidence: 99%

An Improved Type-2 Possibilistic Fuzzy C-Means Clustering Algorithm with Application for MR Image Segmentation

Chen¹,

Li²,

Li³

2016

IJSIP

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Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Cited by 142 publications

References 16 publications

Design of ANFIS Estimator of Permanent Magnet Brushless DC Motor Position for PV Pumping System

Design of ANFIS Estimator of Permanent Magnet Brushless DC Motor Position for PV Pumping System

Application of Adaptive Neural Fuzzy Inference System and Fuzzy C- Means Algorithm in Simulating the 4-Chlorophenol Elimination from Aqueous Solutions by Persulfate/Nano Zero Valent Iron Process

An Improved Type-2 Possibilistic Fuzzy C-Means Clustering Algorithm with Application for MR Image Segmentation

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