ANFIS normally has 5 layers of neurons of which neurons in the same layer are of the same function family.

*Figure 1: Structure of the ANFIS network.*

*Figure 2: ANFIS Architecture*

**Layer 1 (L1):**Each node generates the membership grades of a linguistic label. An example of a membership function is the generalised bell function: $$\mu(x)=\dfrac{1}{1+|\dfrac{x-c}{a}|^{2b}}$$ where {a, b, c} is the parameter set. As the values of the parameters change, the shape of the bell-shaped function varies. Parameters in that layer are called premise parameters.**Layer 2 (L2):**Each node calculates the firing strength of each rule using the min or prod operator. In general, any other fuzzy AND operation can be used.**Layer 3 (L3):**The nodes calculate the ratios of the rule’s firing strength to the sum of all the rules firing strength. The result is a normalised firing strength.**Layer 4 (L4):**The nodes compute a parameter function on the layer 3 output. Parameters in this layer are called consequent parameters.**Layer 5 (L5):**Normally a single node that aggregates the overall outputas the summation of all incoming signals