Adaptive neuro fuzzy inference system: Difference between revisions

Revision as of 07:56, 16 April 2020

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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 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 [[Fuzzy number]]s is done with the membership function which contains of [[T-norm fuzzy logics|semantic descriptions]] like near, middle and far.<ref>{{cite journal |doi=10.1016/j.pisc.2016.04.094 |year=2016 |publisher=Elsevier BV |volume=8 |pages=421–423 |author=Anish Pandey and Saroj Kumar and Krishna Kant Pandey and Dayal R. Parhi |title=Mobile robot navigation in unknown static environments using ANFIS controller |journal=Perspectives in Science |doi-access=free }}</ref> Each possible linguistic value is given by an individual [[Artificial neuron|neuron]]. The neuron “near” fires with a value from 0 until 1, if the distance is located within the category "near". While the neuron “middle” fires, if the distance in that category. The input value “distance in pixels” is split into three different neurons for near, middle and far.

==References==