Bias

In the mathematical model of artificial neural networks, bias is an added constant parameter separate from the weights that enables the activation function to be shifted independently of the input space. Functionally, this value ensures that the neuron can still generate a relevant output even when the sum of input signals is zero or low, thereby increasing the model's fitting flexibility to the training data. The main advantage of applying bias is that it allows the decision boundary to be positioned more precisely in the coordinate system, which is essential for solving complex, non-linear problems and accelerating the convergence of the learning process.