3.3.2. ANN

The ANN model was used to solve the non-linear problem. The strong self-learning ability of the ANN can generate accurate predictions with high computational speed. Many kinds of neutral networks have been established, and multilayer feed forward is one of the most popular. Back propagation (BP) is one of commonly used algorithm, which can minimize ANN error properly [62]. Hence, the back propagation (BP) neutral network was adopted in this study.

Although ANN has been widely and successfully used in many studies, it has several disadvantages. For example, it is tedious to choose the number of hidden layers and the number of nodes at the hidden layer; the learning rate of ANN is usually decided randomly; and it is more likely to achieve local minima rather than global minima [63–65]. Moreover, the proper selection of parameters such as learning rate and momentum coefficient are important for model convergence progress. Hence, in this study, genetic algorithm (GA) was utilized to designate the parameters of the ANN model including the number of hidden layers, the number of nodes at the hidden layer, learning rate, and the momentum coefficient.
