پیش‌بینی متغیرهای داخلی یک گلخانه دوطرفه شیشه‌ای با ساختار ویژه با استفاده از مدل‌های شبکه عصبی (mlp و rbf)

یکی از مهم‌ترین اقدامات به‌منظور ایجاد محیطی مناسب برای رشد بهینه گیاه گلخانه‌ای، کنترل دقیق متغیرهای داخلی گلخانه به‌منظور بهینه‌سازی میزان تبادلات انرژی بین گلخانه با محیط بیرون است. در این راستا یکی از ایده‌های جدید، هوشمندسازی گلخانه است. اولین قدم در این مسیر، پیش‌بینی تمامی متغیرهای تاثیرگذار یک گلخانه به حساب می‌آید. از این رو در این تحقیق از دو مدل شبکه عصبی (mlprbf) برای تخمین چهار متغیر تاثیرگذار (شامل دمای هوا، گیاه، سقف و رطوبت هوای داخل گلخانه) یک گلخانه دوطرفه شیشه‌ای با ساختار ویژه واقع در شهر ملاثانی اهواز استفاده شد. بدین منظور از متغیرهای محیطی بیرونی شامل دما و رطوبت هوای بیرون و همچنین تابش روی سطح افق به‌عنوان فاکتورهای ورودی مدل‌ها، استفاده شد. داده‌برداری توسط حسگرهای مربوطه در گلخانه‌ای به مساحت (12 مترمربع) و حجم هوای (40 مترمکعب) انجام گرفت. نتایج نشان داد که مدل rbf از دقتی حدود 50% بیش‌تر نسبت به مدل mlp برخوردار است. در این تحقیق قابلیت تعمیم‌پذیری هر دو مدل با 80 و 40 درصد از کل داده‌های آموزشی مورد بررسی قرار گرفت. نتایج به‌دست آمده نشان داد که مدل rbf قادر است با مجموعه داده‌های کم‌تر نسبت به مدل mlp، نتایج را بهتر و دقیق‌تر برآورد کند. دقت بالاتر و قابلیت کار با مجموعه داده‌های کم‌تر از مزایای شناخته شده مدل rbf در این تحقیق بود که می‌تواند در هوشمند کردن گلخانه‌های نسل جدید و کنترل آن‌ها مورد استفاده قرار گیرد.

Prediction the inside variables of evenspan glass greenhouse with special structure by artificial neural network (MLPRBF) models

Introduction Controlling greenhouse microclimate not only influences the growth of plants, but is also critical in the spread of diseases inside the greenhouse. The microclimate parameters are inside air, roof, crop and soil temperature, relative humidity, light intensity, and carbon dioxide concentration. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models and also artificial neural networks (ANNs) are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Usually thermal simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. So the main objective of this paper is comparison between two types of artificial neural networks (MLP and RBF) for prediction 4 inside variables in an evenspan glass greenhouse and help the development of simulation science in estimating the inside variables of intelligent greenhouses. Materials and methods In this research, different sensors were used for collecting the temperature, solar, humidity and wind data. These sensors were used in different positions inside the greenhouse. After collecting the data, two types of ANNs were used with LM and Br training algorithms for prediction the inside variables in an evenspan glass greenhouse in Mollasani, Ahvaz. MLP is a feedforward layered network with one input layer, one output layer, and some hidden layers. Every node computes a weighted sum of its inputs and passes the sum through a soft nonlinearity. The soft nonlinearity or activity function of neurons should be nondecreasing and differentiable. One type of ANN is the radial basis function (RBF) neural network which uses radial basis functions as activation functions. An RBF has a single hidden layer. Each node of the hidden layer has a parameter vector called center. This center is used to compare with the network input vector to produce a radially symmetrical response. Responses of the hidden layer are scaled by the connection weights of the output layer and then combined to produce the network output. There are many types of crossvalidation, such as repeated random subsampling validation, Kfold crossvalidation, K×2 crossvalidation, leaveoneout crossvalidation and so on. In this study, we pick up Kfold cross validation for selecting parameters of model. The Kfold crossvalidation is a technique of dividing the original sample randomly into K subsamples. Different performance criteria have been used in literature to assess model’s predictive ability. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2) are selected to evaluate the forecast accuracy of the models in this study. Results and discussion The results of neural networks optimization models with different networks, dependent on the initial random values of the synaptic weights. So, the results in general will not be the same in two different trials even if the same training data have been used. So in this research Kfold cross validation was used and different data samples were made for train and test of ANN models. The results showed that trainlm for both of MLP and RBF models has the lower error than trainbr. Also MLP and RBF were trained with 40 and 80% of total data and results indicated that RBF has the lowest sensitivity to the size data. Comparison between RBF and MLP model showed that, RBF has the lowest error for prediction all the inside variables in greenhouse (Ta, Tp, Tri, Rha). In this paper, we tried to show the fact that innovative methods are simple and more accurate than physical heat and mass transfer method to predict the environment changes. Furthermore, this method can use to predict other changes in greenhouse such as final yield, evapotranspiration, humidity, cracking on the fruit, CO2 emission and so on. So the future research will focus on the other soft computing models such as ANFIS, GPR, Time Series and … to select the best one for modeling and finally online control of greenhouse in all climate and different environment.