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Draw the five layer architecture of ANFIS and explain each layer in brief.

Mumbai University > Computer Engineering > Sem 7 > Soft Computing

Marks: 5 M

Year: Dec 2015

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

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Figure 1: Structure of the ANFIS network.

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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
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