Thermal Food Processing

Modeling Food Thermal Processes Using Artificial Neural Networks 113
the network’s weights and thresholds so as to minimize the error in its prediction
on the training set, mathematically defined as follows:
εi = di −ci
(4.8)
where ε i is the output error, d i is the desired output, and c i is the calculated output,
for the i th neuron on the output layer only. The total square error on the output
layer can be calculated as
∑ ∑
E = ε = d −c
2 ( )
(4.9)
The change in the weight factor for the j th connection to the i th neuron is
obtained by
∆w
(4.10)
where η is a linear proportionality constant, called the learning rate (typically,
0 < η