مدل‌سازی فازی- عصبی و سطح پاسخ آبگیرى اسمزی دانه‌های انار

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه علوم کشاورزی و منابع طبیعی گرگان

2 دانشگاه فردوسی مشهد

چکیده

در این پژوهش دانه‌های انار به روش اسمزی، با محلول‌های40، 50 و 60 درصد ساکارز در دماهای 45، 55 و 65 درجه سلسیوس فرایند شدند و مقدار جذب موادجامد، کاهش آب و کاهش وزن نمونه‌ها در زمان‌های 60، 120، 180 دقیقه اندازه‌گیری گردید. فرایند آبزدایی اسمزی با ترکیب تکنیک­های منطق فازی و شبکه‌های‌ عصبی‌مصنوعی (مدل‌سازی فازی- عصبی) و روش سطح پاسخ مدل‌سازی شد. برای مدل‌سازی، درون‌یابی و افزایش داده‌ها، از منطق فازی استفاده شد و با وارد کردن نتایج مدل فازی در شبکه‌های عصبی‌مصنوعی، شبکه پس‌انتشار پیشخور با توپولوژی 3-8-3، ضریب‌همبستگی 98344/0 و میانگین مربعات خطای 02278/0 با تابع فعال‌سازی لگاریتمی و الگوی یادگیری لونبرگ – مارکوات به عنوان بهترین مدل عصبی ارائه گردید. مدل‌های رگرسیونی ایجاد شده با استفاده از روش سطح پاسخ نیز با ضریب همبستگی بیش از 91/0 توانایی بالایی برای پیش‌بینی فاکتورهای پاسخ داشتند ولی در مقایسه با مدل‌ فازی- عصبی از دقت پایین‌تری برخوردار بودند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Neuro-fuzzy and response surface modeling of osmotic dehydration of pomegranate arils

نویسندگان [English]

  • Mohammad Ganjeh 1
  • Seyed Mahdi Jafari 1
  • Sajjad Ghaderi 2
چکیده [English]

In this research, pomegranate arils are dehydrated by osmotic dehydration in 40, 50, and 60 % sucrose ‎solutions and at 45, 55 and 65 degrees C‎‏ ‏‎ and Weight Reduction, Solids grain and Water Loss of the products ‎were measured at 60, 120 and 180 minutes of process. Osmotic dehydration processes was modeled by ‎combination of neural network and fuzzy logic techniques (Neuro-fuzzy) and response surface methodology. ‎For modeling, interpolation and increase of the data’s, fuzzy logic was used. By entering the obtained results ‎from fuzzy model into the neural network tool, the Feed-Forward-Back-Propagation network with the ‎topology of 3-8-3 and the correlation coefficient of 0.98344‎‏ ‏and mean square error of 0.02278‎‏ ‏with ‎application of Log-sigmoid transfer function‏ ‏‎(logsig) and Levenberg–Marquardt learning algorithm was ‎determined as the best neural model. Regression models created by response surface methodology by ‎correlation coefficient of 0.90 were also capable for prediction of response factors but in comparison with ‎Neuro-fuzzy models have a lower accuracy.‎

کلیدواژه‌ها [English]

  • modeling
  • neural network
  • fuzzy logic
  • Response surface
  • Pomegranate arils
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