**2.3 Adaptive neuro-fuzzy inference system modeling**

ANFIS, an Adaptive Neuro-Fuzzy Inference System, is an intelligent system that combines the capabilities of Artificial Neural Network (ANN) and Fuzzy Logic Inference System (FIS) to bridge the gap that exists between the two [35–37]. This is a well-established technique that employs relational models to represent linear and nonlinear relationships between input and output parameters, taking into account the fact that human knowledge is often fuzzy and not strictly defined [35]. The function of the human nervous system is depicted by the Fuzzy Inference System (FIS), which is supported by the Artificial Neural Network (ANN). A neuro-fuzzy component forms each layer of the ANFIS, which can be recognized as a feedforward ANN that was developed by [36]. The input variables' activation process will take place via the function parameters, which are trained using an optimization method defined by the input membership function (MF) and then passed on to the next neuron. Following
