*2.3. Building the ANN Model*

#### 2.3.1. Development of Neural Network Model

One of the most commonly used neural network models is the BP neural network, which utilizes the BP algorithm. Even the most complex nonlinear relationship completely approximates it. The information is dispersed and stored in the neurons of the network. The computation is extremely fast due to parallel processing. Since neural networks are self-learning and adaptive, they can deal with uncertain or unknown systems. This system is excellent when simultaneously processing both quantitative and qualitative information. It can coordinate a wide range of input information relations and is, thus, ideal for fusion and multimedia applications. A well-trained artificial neural network

can function as a predictive model for a specific application, which is a data processing system inspired by biological neural systems. The predictive power of an ANN is derived from training on experimental data, which is then validated using independent data. Artificial neural networks can relearn and adapt to improve their performance by updating data availability [27]. The structure and operation of ANNs have been described by numerous authors [28]. The modeling used in feedforward neural networks for prediction was designed to capture the correlation between the historical model inputs and their corresponding outputs. This is accomplished by repeatedly feeding the model examples of input/output relationships and adjusting the model coefficients (i.e., connection weights) to minimize the error function between the historical output and the model-predicted outputs.

This article follows the procedure of the artificial neural network model as described by Maier and Dandy [29]. They include determining model inputs and outputs, dividing and preprocessing available data, selecting an appropriate network architecture, optimizing connection weights (training), setting stopping criteria, and validating the model. A typical algorithm flow diagram is shown in Figure 2. In this work, all calculations and programming were executed in MATLAB (R2016a, 9.0.0.341360). The data used to calibrate and validate the neural network model were obtained from the bench field measurements of the flexible threshing experiment device and the corresponding information on the feeding amount and material characteristics. The data cover a wide range of variation in different operating parameters types and threshing properties. The database comprises a total of 25 individual cases. The statistics of the input and output parameters used for the artificial neural networks are shown in Table 3. Figure 3 is a database of all the threshing performance metrics for the ANN.

**Figure 2.** The artificial neural network algorithm flow chart.

**Table 3.** Statistical criteria of the input parameters and performance attributes (output parameters) used in the ANN model.


**Figure 3.** The threshing performance index database for the artificial neural network.
