Modeling and optimization of viscosity in enzyme-modified cheese by fuzzy logic and genetic algorithm

Mohebbi, Mohebbat; Barouei, J.; Akbarzadeh-T, M.-R.; Rowhanimanesh, Alireza; Najafi, Mohammad B Habibi; Yavarmanesh, Masoud

doi:10.1016/j.compag.2008.01.010

Cited by 27 publications

(14 citation statements)

References 19 publications

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“…A Takagi-Sugeno-Kang (TSK) fuzzy model was applied to generate the ANFIS. In TSK model the independent variables which enter to the fuzzy inference engine of the system are fuzzy sets but the output of engine is a crisp data (Mohebbi et al, 2008). Each element in a fuzzy set has a membership value between 0 and 1 (Cakmakci, 2007).…”

Section: Development Of Primary Anfis Modelsmentioning

confidence: 99%

“…Mechanisms involved in many of food processing operations including sonodisruption process of casein micelles are usually ill-defined and complicated. It is therefore difficult to model the phenomena by differential equations at the present (Kilimann et al, 2005) while, the fuzzy approach seems more convincing in this regard (Mohebbi et al, 2008). Fuzzy inference systems can correctly describe the complex and non-linear phenomena (Gänzle et al, 2007) with the precise rules.…”

Section: Introductionmentioning

confidence: 98%

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A network-based fuzzy inference system for sonodisruption process of re-assembled casein micelles

Madadlou

Emam‐Djomeh

Mousavi

et al. 2010

Journal of Food Engineering

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Section: Development Of Primary Anfis Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

A network-based fuzzy inference system for sonodisruption process of re-assembled casein micelles

Madadlou

Emam‐Djomeh

Mousavi

et al. 2010

Journal of Food Engineering

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“…Yalcin and Tasdemir (2007) developed a fuzzy expert system (FES) to determine the α‐linolenic acid content of eggs, obtained from hens fed dietary flax seeds. In addition to these researches, other studies have been carried out in different subjects (Krasnov and others 2005; Reshetnikova and others 2007; Mohebbi and others 2008; Samhouri and others 2009). …”

Section: Introductionmentioning

confidence: 99%

Prediction of Effect of Natural Antioxidant Compounds on Hazelnut Oil Oxidation by Adaptive Neuro‐Fuzzy Inference System and Artificial Neural Network

Yalçın¹,

Öztürk²,

Karaman³

et al. 2011

Journal of Food Science

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In this study, natural compounds including gallic acid, ellagic acid, quercetin, β-carotene, and retinol were used as antioxidant agents in order to prevent and decrease oxidation in hazelnut oil. Quercetin showed the strongest antioxidative effect among the antioxidative agents, during storage. The accuracy of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models was studied to estimate the oil samples' peroxide value (PV), free fatty acid (FFA), and iodine values (IV). The root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R(2)) statistics were used to evaluate the models' accuracy. Comparison of the models showed that the ANFIS model performed better than the ANN and multiple linear regressions (MLR) models for estimating the PV, FFA, and IV. The values of R(2) and RMSE were found to be 0.9966 and 2.51, 0.6269 and 88.55, 0.5120 and 101.8 for the ANFIS, ANN, and MLR models for PV in testing period, respectively. The MLR was found to be insufficient for estimating various properties of the oil samples.

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“…However, in many manufacturing processes, it is difficult to find probability distributions for dependent variables. Artificial neural networks [4,15,20,30,40,45] and fuzzy logic modeling techniques [1,10,18,19,33,47] have been used to develop process models in various manufacturing processes. These approaches normally require a large amount of experimental data to develop models, which are sometimes not available in manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

Chan¹,

Kwong²,

Fogarty³

2010

Information Sciences

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a b s t r a c tFuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka's FR and Peters' FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods.

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Modeling and optimization of viscosity in enzyme-modified cheese by fuzzy logic and genetic algorithm

Cited by 27 publications

References 19 publications

A network-based fuzzy inference system for sonodisruption process of re-assembled casein micelles

A network-based fuzzy inference system for sonodisruption process of re-assembled casein micelles

Prediction of Effect of Natural Antioxidant Compounds on Hazelnut Oil Oxidation by Adaptive Neuro‐Fuzzy Inference System and Artificial Neural Network

Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

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