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Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system

Paper ID Volume ID Publish Year Pages File Format Full-Text
19758 43125 2015 8 PDF Available
Title
Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system
Abstract

Knowledge of rheological properties of honey is of great interest to honey handlers, processors and keepers. In this study, genetic algorithm–artificial neural network (GA–ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the viscosity of four types of honey, two poly floral (Mountain, Forest) and two monofloral (Sunflower, Ivy). The GA–ANN and ANFIS were fed with 3 inputs of water content (15.25–19.92%), temperature (10–30 °C) and shear rate (1–42 s−1). The developed GA–ANN, which included 11 hidden neurons, could predict honey viscosity with correlation coefficient of 0.997. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.999). Sensitivity analysis results showed that temperature was the most sensitive factor for prediction of honey viscosity. Both GA–ANN and ANFIS models predictions agreed well with testing data sets and could be useful for understanding and controlling factors affecting viscosity of honey.

Keywords
Fuzzy; Genetic algorithm; Rheology; Sensitivity analysis; Simulation; Honey
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Publisher
Database: Elsevier - ScienceDirect
Journal: Food Bioscience - Volume 9, 1 March 2015, Pages 60–67
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us