Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness

Dong, Minggang; Wang, Ning

doi:10.1016/j.apm.2010.07.048

Cited by 81 publications

(26 citation statements)

References 19 publications

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“…The performance of the SVM model is particularly vulnerable to the parameter g of RBF kernel function and the regularisation constant c which determines the tradeoff between minimising the training error and minimising the model complexity (Chen et al 2007). The leave-one-out cross-validation (LOO-CV) (Dong & Wang 2011) was applied for parameters optimisation of c and g Number of PLS components Number of PLS components subjects to maximise the identification rate. In this research, [2 -8 , 2 8 ] and 0.5 were selected as the range and step of parameters of c and g, respectively.…”

Section: Resultsmentioning

confidence: 99%

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

et al. 2014

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Han F., Huang X., Teye E., Gu H., Dai H., Yao L. (2014): A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms. Czech J. Food Sci., 32: 532-537.Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4°C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction set. Partial least square (PLS) and support vector regression (SVR) were applied comparatively to predict the TVB-N and TVC values. The quantitative models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in the prediction set (R pre ). The results revealed that SVR model was superior to PLS model with RMSEP = 5.65 mg/100 g, R pre = 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, R pre = 0.904 for TVC prediction. This study demonstrated that the electronic tongue together with SVM and SVR has a great potential for a convenient and nondestructive detection of fish freshness.

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Section: Resultsmentioning

confidence: 99%

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

et al. 2014

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“…Findings showed that there was a good consistency found between the experimental results and the ANFIS model. Dong and Wang [18] applied the ANFIS model with leave-one-out crossvalidation method for prediction of surface roughness using also Lo's experimental results [14]. Based on a comparison between other literatures results and the results obtained by them through the ANFIS clustering method, the researchers gained better accuracy by using the proposed method than the others did.…”

Section: Introductionmentioning

confidence: 98%

Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

Al-Zubaidi

Ghani

Haron

2013

Modelling and Simulation in Engineering

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Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end milling Ti6Al4V alloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing of ANFIS models, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability of ANFIS in modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results.

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“…The ANFIS model with bell-shaped membership function distributions for the input variables was found to yield the best results amongst all. Minggang Dong and Ning Wang [12] proposed to model surface roughness with ANFIS and leave-one-out cross-validation (LOO-CV) approach. This approach focuses on both architecture and parameter optimization in order to improve the prediction accuracy in end milling process.…”

Section: Previous Workmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Interference System Modelling of EDM Process Using CNT Infused Copper Electrode

Sethuramalingam¹,

Sundararaj²

2017

IJIES

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This study deals with the experimental investigation of the machining characteristics of AISI D2 Tool Steel on the EDM process by using Carbon Nanotube (CNT) infused nano copper electrode, and checking the improvement in machinability characteristics like Material Removal Rate (MRR), Electrode wear rate (EWR) and Surface finish (SR). The work material is spark machined with pure copper and CNT-infused copper electrodes by varying parameters on an EDM. Experiment work is conducted based on Taguchi L9 orthogonal array. The various results of the machining characteristics of with and without CNT are compared and analyzed by means of Adaptive Neuro-Fuzzy Inference System (ANFIS) approach; Analysis of variance (ANOVA) and F-test are used to check the validity of regression model and determine the significant parameter affecting the surface roughness. The output values are found to be close to predicted values with an error of less than 5%.

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Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness

Cited by 81 publications

References 19 publications

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

Adaptive Neuro-Fuzzy Interference System Modelling of EDM Process Using CNT Infused Copper Electrode

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