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==ANFIS architecture==
It is possible to identify two parts in the network structure, namely premise and consequence parts. In more details, the architecture is composed by five layers.
# The first layer takes the input values and determines the [[Membership function (mathematics)|membership functions]] belonging to them. It is commonly called fuzzification layer. The membership degrees of each function are computed by using the premise parameter set, namely {a,b,c}. # The second layer is responsible of generating the firing strengths for the rules. Due to its task, the second layer is denoted as "rule layer". # The role of the third layer is to normalize the computed firing strengths, by dividing each value for the total firing strength. # The fourth layer takes as input the normalized values and the consequence parameter set {p,q,r}. # The values returned by this layer are the defuzzificated ones and those values are passed to the last layer to return the final output.<ref name="KarabogaKaya2018">{{cite journal|last1=Karaboga|first1=Dervis|last2=Kaya|first2=Ebubekir|title=Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey|journal=Artificial Intelligence Review|volume=52|issue=4|pages=2263–2293|year=2018|issn=0269-2821|doi=10.1007/s10462-017-9610-2|s2cid=40548050}}</ref> ===Fuzzification layer===
The first layer of an ANFIS network describes the difference to a vanilla neural network. Neural networks in general are operating with a [[data pre-processing]] step, in which the [[Feature (machine learning)|features]] are converted into normalized values between 0 and 1. An ANFIS neural network doesn't need a [[sigmoid function]], but it's doing the preprocessing step by converting numeric values into fuzzy values.<ref>{{cite journal |doi=10.1109/72.159060 |pmid=18276470 |year=1992 |publisher=Institute of Electrical and Electronics Engineers (IEEE) |volume=3 |number=5 |pages=714–723 |author=J.-S.R. Jang |title=Self-learning fuzzy controllers based on temporal backpropagation |journal=IEEE Transactions on Neural Networks }}</ref>
Here is an example: Suppose, the network gets as input the distance between two points in the 2d space. The distance is measured in pixels and it can have values from 0 up to 500 pixels. Converting the numerical values into [[
==References==
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