Adaptive neuro fuzzy inference system: Difference between revisions

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==ANFIS architecture==

It is possibile 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 diving 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|year=2018|issn=0269-2821|doi=10.1007/s10462-017-9610-2}}</ref>

==References==