اندازه‌گیری دبی جرمی شلتوک با استفاده از حسگر خازنی و مدل سازی آن با رگرسیونی چندگانه، ANN و ANFIS

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجو/ دانشگاه محقق اردبیلی

2 هیات علمی/ دانشگاه محقق اردبیلی

3 هیات علمی / دانشگاه علوم کشاورزی و منابع طبیعی ساری

چکیده

اندازه­گیری دبی جرمی با استفاده از حسگرهای خازنی به عنوان روش ارزان و سریع توسعه یافته است. اما پیش­بینی دبی جرمی به علت وابستگی پاسخ حسگر به عوامل مختلف و پیچیدگی اثر این عوامل دشوار است؛ لذا در این مطالعه پتانسیل شبکه عصبی مصنوعی (ANN)، سیستم استنتاج فازی (ANFIS)  و تکنیک‌های رگرسیون چندگانه (MR)  برای پیش‌بینی دبی جرمی شلتوک با استفاده از سنسور خازنی مورد بررسی قرار گرفت. بسامد، رطوبت و ولتاژ خروجی به عنوان متغیرهای ورودی و دبی جرمی به عنوان خروجی در توسعه مدل­ها به کار گرفته شد. نتایج نشان داد که ANN دارای بالاترین ضریب همبستگی بین مقادیر پیش بینی شده و واقعی است (927/0R2 =). ضریب همبستگی بین مقادیر پیش بینی شده و واقعی برای ANFIS برابر با (909/0=( R2  است. نتایج نشان می‌دهد که تکنیک‌های ANN و ANFIS به طور بالقوه می‌تواند برای پیش بینی دبی جرمی محصولات کشاورزی مورد استفاده قرار گیرند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Measuring of Paddy mass flow using capacitive sensor and modeling with using multiple regression, ANN, and ANFIS models

نویسندگان [English]

  • Mohammad Tahmasebi 1
  • Reza Tabatabaei-kolor 3
چکیده [English]

Measuring the mass flow of agricultural products by using capacitive sensors as inexpensive and rapid method has been developed. But predicted mass flow due dependence of sensor response to various factors and complexity effect of these factors is difficult. So in this study the potential of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Regression (MR) techniques to predict paddy mass flow by using capacitive sensor was evaluated. Frequency, Moisture content and output voltage were employed as input variables and mass flow was considered as output in the developed models. Results showed that ANN gave the best correlation between predicted and actual values (R2 = 0.927); ANFIS gave the correlation between predicted and actual values, with correlation coefficient (R2) of 0.909. These results indicate that the ANN and ANFIS techniques can potentially be used to predict mass flow of agricultural products.

کلیدواژه‌ها [English]

  • Capacitive sensor
  • Mass flow
  • ANN
  • MR
  • ANFIS
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