## 3 Application of Artificial Neural Networks for the Prediction of the Greenhouse Microclimate
### Feedforward Neural Networks Models for Prediction of Microclimate in Greenhouse  
[PERSON] et al. [112] modeled the indoor temperature of a hydroponic greenhouse based on indoor relative humidity, outdoor air temperature, and solar radiation. They discuss different training methods for a neural network of radial base function (RBF) that are structurally simpler than multilayer perceptron (MLP), which are a type of FFNNs. The objective of using a radial base function artificial neural network (RBFANN) is that the design and training process is a simpler task. The training methods compared are the off-line and on-line, mainly differentiated in that the use of the learning algorithm adjusts the free network parameters as the output or input data are determined, respectively. In the study, they concluded that for both off-line and on-line training, better results are obtained by applying the Levenberg-Marquardt (LM) method, which is the best online. Other works applied the RBFANNs, as is the case of [PERSON] et al. [99] who presented an adaptive proportional and derivative control (PD) scheme based on the RBF neural network. The RBF network used it to adjust the parameters of the PD controller using the Jacobian information for the greenhouse climate control problem. The results showed that the proposed adaptive controller obtained a more satisfactory performance than a conventional PD scheme and was even considered for application in non-linear dynamic systems such as the climate system of a greenhouse. Furthermore, [PERSON] et al. [38] presented a control strategy that combines RBF with PID, for greenhouse climate control. They compared the proposed adaptive online adjustment method with the offline adjustment scheme that uses GA to find the optimal gain parameters based on the error criteria. Offline learning consists on adjusting weight vectors and network thresholds after the entire training set is presented (requires at least one training data stage), while in online learning network weights and threshold adjustments are made after each training sample is submitted (after executing the adjustment step, the sample can be discarded) [122]. Interesting results were obtained such as better set point monitoring performance, a smoother control process characterized by smaller oscillatory amplitudes that the control can be applied in real time online and that the control scheme adapts well to fluctuations in external climate. Regarding the MLP networks, which are a type of FFNNs, [PERSON] et al. [123] used them with training based on a gradient BP algorithm to predict the internal temperature and internal humidity within a tomato greenhouse from external climatic data (external humidity, total radiation, wind direction, wind speed, and external temperature). When comparing the results obtained from the network with a multiple linear regression method (MLR), the prediction of the MLP network proved to be significantly better. Also, [PERSON] et al. [60] proposed a BP network based on principal component analysis (PCA) to predict the indoor humidity in a greenhouse. The PCA values were taken as inputs from the back propagation neural network (BPNN), the objective of the PCA was to simplify the data samples and make the model have a faster learning speed. The predicted humidity coincided well with the measurement, which showed that the model had high accuracy. Furthermore, they compared the PCA-based BPNN method with a stepwise regression method and observed that the PCA-based BPNN performed better. Likewise, [PERSON] et al. [124] used four MLP architectures with learning algorithms based on gradient descent momentum (GDM) and LM to predict roof temperature, indoor air humidity, soil temperature and soil moisture of a greenhouse. The results obtained showed that the prediction error is low and when compared with predictions obtained through regression models, the error used to predict the four parameters were approximately two times greater than the MLP method.