2.3.4. Model Architecture

Determining the network architecture is one of the crucial and challenging tasks in the development of ANN models because it requires the selection of several hidden layers and the number of nodes in each of these.

The number of model inputs and outputs restricts the number of nodes in the input and output layers. The input layer of the ANN model developed in this work had four nodes, one for each of the model inputs (i.e., a rotational speed of cylinder (RS), threshing clearance of concave sieve (TC), separating clearance of concave sieve (SC), feeding quantity (FQ)). On the other hand, the output layer had three nodes (i.e., crushing rate (YP), impurity rate of threshed materials (YZ), and entrainment loss rate (YS)) representing the measured value of threshing performance.

Figure 5 shows the basic elements of an artificial neuron. Artificial neurons mainly comprise weight bias and activation functions. The BP neural network is the most popular and widely used artificial neural network architecture [33]. It involves an input layer, one or more hidden layers, and an output layer. Evidence suggests that a network with a threshold, at least one S-shaped hidden layer, and a linear input layer can approximate any rational number [34]. Mathematical expressions and interpretations of artificial neural networks can be referred to in reference [35].

**Figure 5.** Schematic diagram of an artificial neural network.

The activation function introduces nonlinearity into the neural network, making it more powerful than the linear transformation. The Levenberg–Marquardt algorithm is the most commonly used multi-layer perception training algorithm. It is a gradient descent technique [36] used to reduce the error of specific training patterns. The network was built using the Levenberg–Marquardt backpropagation technique. Tansig is a common nonlinear activation function for nodes in the hidden layer. Figure 6 depicts the architecture of the artificial neural network system described in this paper. *W* is a weight matrix for the hidden and output layers, and *Nij* is a node that computes a weighted sum of its inputs and passes the sum through a soft nonlinearity or activity function.

**Figure 6.** The artificial neural network architecture of threshing performance.
