مقایسۀ نتایج مدل‌های شبکۀ عصبی مصنوعی با مدل‌های ریاضی مختلف برای تخمین نرخ نم در فرایند خشک‌کردن میوۀ به

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه پیام نور

2 پژوهشکده توسعه صنایع شیمیایی

چکیده

در این پژوهش، فرایند خشک‌کردن میوۀ به و تأثیر مشخصه‌های مختلفی مانند سرعت هوای خشک‌کردن، زمان، دما و ضخامت بر نسبت نم،  مطالعه و بررسی شد. 7 مدل‌ ریاضی بر داده‌های به دست آمده از 27 سری آزمایش برازش و بهترین مدل انتخاب شد. همچنین مدل‌سازی با شبکۀ عصبی مصنوعی (ANN) انجام گرفت. در این مدل‌سازی، اثر تمام مشخصه‌های ورودی در فرایند خشک‌کردن به‌طور همزمان بررسی شد. ساختار شبکۀ انتخابی از نوع پرسپترون چندلایه با الگوریتم پس انتشار خطا در نظر گرفته شد. با پژوهش روی تعداد مختلفی از نرون‌های لایۀ میانی و نیز توابع انتقال مختلف،‌ از 9 نرون و تابع انتقال لگاریتم سیگموئیدی برای لایۀ میانی و تابع انتقال پیورلین برای لایۀ خروجی استفاده شد. مدل‌سازی با شبکۀ عصبی مصنوعی،اثر همزمانچهارمشخصۀورودی را با دقت بسیار بالایی پیش‌بینی کرد. نتایج نشان داد که مدل‌سازی ANN در مقایسه با بهترین مدل‌ ریاضی دارای دقت بالاتری است.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of Artificial Neural Network and Different Mathematical Models for Estimation of Moisture Rate in Quince Fruit Drying

نویسندگان [English]

  • A. Khoshhal 1
  • H. Yazdani 1
  • N. S. Mousavi 2
1 Payame Noor University
2 Iranian Institute of Research & Development in Chemical Industries (IRDCI-ACECR)
چکیده [English]

In this research, the process of drying the quince fruit and the effect of various parameters such as the drying air speed, time, temperature and thickness on moisture ratio were studied. 7 mathematical models were fitted to the data obtained from 27 series of experiments and the best model was selected. Modeling was also performed by artificial neural network. In this modeling, the effect of all input parameters on the drying process was investigated simultaneously. The selective network structure was considered multi-layer perceptron with the back-propagation algorithm. By researching the number of different hidden layer neurons and different transfer functions, 9 neurons and "logsig” transfer function were used for the hidden layer and "purelin” transfer function for the output layer. Modeling by artificial neural network predicted the simultaneous effect of the four input parameters with very high accuracy. The results showed that ANN modeling had better accuracy than the best mathematical model.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • Moisture Ratio
  • Mathematical Model

 

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