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
1
2
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
Abraham, A. (2005). Adaptation of Fuzzy Inference System Using Neural Learning. Fuzzy Systems Engineering. Series Studies in Fuzziness and Soft Computing, 181, 53-83.
Arslan, S., Inanc, F., Gary. J. M. & Colvin. T. S. (2000). Grain flow measurements with x-ray techniques. Computers and Electronics in Agriculture. 26, 65-80.
ASAE Standards. (1994). S352.2. Moisture measurement ungrounded grains and seeds. St. Joseph, MI, ASAE.
Aydin, C., Ogut, H. & Konak, M. (2002). Some physical properties of Turkish Mahaleb. Biosystems Engineering, 82 (3), 231-234.
Balasubramanian, D. (2002). Physical properties of raw cashew nut. Journal of Agricultural Engineering Research, 78, 291-297.
Berbert, P. A., Queiroz, D.M. & Melo, E.C. (2002). Dielectric properties of common bean. Biosystems Engineering, 83 (4), 449–462.
Cohen, S. & Intrator, N. (2002). Automatic model selection in a hybrid perceptron/radial network. Information Fusion, 3 (4), 259–266.
Debye, P. (1929). Polar Molecules, Dover Publication, Inc, New York.
Eubanks, J. C. & Birrell, S. J. (2001). Determining moisture content of hay and forages using multiple frequency parallel plate capacitors. ASAE Paper. 011072.
Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23 (3), 665-685.
Kenneth, J., Wernter, S. & MacInyre, J. (2001). Knowledge extraction from Radial Basis Function networks and Multi-layer Perceptron’s. International Journal of Computational Intelligence and Applications, 1 (3), 369–382.
Klemme, K. A., Schumacher, J. A. & Donell, D. P. (1992). Results and advantages of a especially variable technology for crop yield. ASAE Paper, 921651.
Kumhala, F., Kviz, Z., Kmoch, J. & Prosek. V. (2007). Dynamic laboratory measurement with dielectric sensor for forage mass flow determination. Research in Agricultural Engineering, 53 (4), 149–154.
Kumhala, F., Prosek, V. & Blahovec. J. (2009). Capacitive throughput sensor for sugar beets and potatoes. Biosystems engineering, 102, 36-43.
Kumhala, F., Prosek, V. & Kroulik, M. (2008). Parallel Plate Mass Flow Sensor for Forage Crops and Sugar Beet. ASAE Paper, 084700.
Kumhala, F., Prosek, V. & Kroulik, M. (2010). Capacitive sensor for chopped maize throughput measurement. Computers and Electronics in Agriculture, l (70), 234–238.
Lawrence, K. C., Funk, D. B. & Windham, W. R. (2001). Dielectric moisture sensor for cereal grains and soybeans. Transaction of ASAE, 44 (6), 1691-1696.
Lim, T. S., Loh, W. Y., Tim, L. & Shih, Y. S., (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithm. Machine Learning, 40(3), 203–238.
Nazemsadat, S. M. R. & Loghavi, M. (2013). Design, Development and Evaluation of a Mass Flow Sensor for Grain Combine Harvesters. Journal of Agricultural Machinery. 32, 71-84. (in Farsi)
Nelson, S. O. (2006). Agricultural applications of dielectric measurements. IEEE Transactions on dielectrics and Electrical Insulation, 16, 688-702.
Nelson, S. O. (2008). Dielectric properties of agricultural products and some applications. Research in Agricultural Engineering, 54, 104-112.
Osman, A. M., Savoie, P., Grenier, D. & Theriault, R. (2002). Parallel-plate capacitance moisture sensor for hay and forage. ASAE Paper, 021055.
Rostampour, V., Motlagh, A. M, Komarizadeh, M. H., Sadeghi, M., Bernousi, I. & Ghanbari, T. (2013). Using Artificial Neural Network (ANN) technique for prediction of apple bruise damage. Australian Journal of Crop Science, 7(10), 1442-1448.
Schnug, E. D. M., Murphy, E., Evans, S. Haneklaus. & Lamp. J. (1993). Yield mapping and application of yield maps to computer-aided local resource management: P. C. Robert, R. H. Rust and W. E. Larson, Ed., Proceedings of Soil Specific Crop Management, 87-93.
Simpson, P. K. (1990). Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations. Pergamum Press, New York.
Soltani, M., Alimardani, R. & Omid, M. (2011). A Feasibility Study of Employing a Capacitance Based Method in Banana Ripeness Recognition. Iranian Journal of Biosystems Engineering, 42 (1), 21-27. (In Farsi)
Stafford, J. V., Ambler, B., Lark, R. M. & Catt, J. (1996). Mapping and interpreting the yield variation in cereal crops. Computers and Electronics in Agriculture, 14 (2-3): 101-119.
Taghinezhad, J., Alimardani, R. & Jafari, A. (2012). Development of a Capacitive Sensing Device for Prediction of Water Content in Sugarcanes Stalks. International Journal of Advanced Science and Technology, 44, 61-68.
Vakil-Baghmisheh, M.T. (2002). Farsi Character recognition using artificial neural networks, Ph. D. dissertation, University of Ljubljana, Slovenia.
Venkatesh, M. S. & Raghavan, G. S. V. (2005). An overview of dielectric properties measuring techniques. Canadian Biosystems Engineering, 47 (7), 15-30.
Wagner, L. E. & Schrock, M. D. (1989). Yield determination using a pivoted auger flow sensor. Transactions of the ASAE, 32 (2), 409-413.