Content deleted Content added
Split the paragraph into a list for better readability |
|||
| (23 intermediate revisions by 21 users not shown) | |||
Line 1:
{{Short description|Type of artificial neural network}}
An '''adaptive neuro-fuzzy inference system''' or '''adaptive network-based fuzzy inference system''' ('''ANFIS''') is a kind of [[artificial neural network]] that is based on Takagi–Sugeno fuzzy [[inference system]]. The technique was developed in the early 1990s.<ref>{{cite conference |last=Jang |first=Jyh-Shing R |title=Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm |year=1991|conference=Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, July 14–19 |volume=2 |pages=762–767 |url=http://www.aaai.org/Papers/AAAI/1991/AAAI91-119.pdf }}</ref><ref>{{cite journal |last=Jang |first=J.-S.R. |year=1993 |title=ANFIS: adaptive-network-based fuzzy inference system |journal=IEEE Transactions on Systems, Man and Cybernetics |volume=23 |issue=3 |pages=665–685 |doi=10.1109/21.256541 |url=https://semanticscholar.org/paper/17cf6cf3c51b7cbc6b9ff5a0d1624e3c9928a951 }}</ref> Since it integrates both neural networks and [[fuzzy logic]] principles, it has potential to capture the benefits of both in a single [[:wikt:framework|framework]]. Its inference system corresponds to a set of fuzzy [[Conditional (programming)|IF–THEN rules]] that have learning capability to approximate [[nonlinear function]]s.<ref>{{Citation |chapter=Adaptation of Fuzzy Inference System Using Neural Learning |title=Fuzzy Systems Engineering: Theory and Practice |first=A. |last=Abraham |year=2005 |editor-first=Nadia |editor1-last=Nedjah |editor2-first=Luiza |editor2-last=de Macedo Mourelle |series=Studies in Fuzziness and Soft Computing |volume=181 |publisher=Springer Verlag |___location=Germany |doi=10.1007/11339366_3 |pages=53–83 |isbn=978-3-540-25322-8 |citeseerx=10.1.1.161.6135 }}</ref> Hence, ANFIS is considered to be a [[universal estimator]].<ref>Jang, Sun, Mizutani (1997) – Neuro-Fuzzy and Soft Computing – Prentice Hall, pp 335–368, {{ISBN|0-13-261066-3}}</ref> For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by [[genetic algorithm]].<ref>{{cite journal|last=Tahmasebi|first=P. |title=A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation |year=2012 |journal=Computers & Geosciences |volume=42 |pages=18–27 |doi=10.1016/j.cageo.2012.02.004 |pmid=25540468 |pmc=4268588 |bibcode=2012CG.....42...18T }}</ref><ref>{{cite journal|last=Tahmasebi|first=P. |title=Comparison of optimized neural network with fuzzy logic for ore grade estimation |year=2010 |journal=Australian Journal of Basic and Applied Sciences |volume=4 |pages=764–772 |url=https://www.researchgate.net/publication/266881168 }}</ref> It has uses in intelligent situational aware energy management system.<ref>{{Cite journal |doi = 10.1109/JSTSP.2018.2848624|bibcode = 2018ISTSP..12..806K|title = Intelligent Soft Computing-Based Security Control for Energy Management Architecture of Hybrid Emergency Power System for More-Electric {{sic|nolink=y|Aircrafts}}|journal = IEEE Journal of Selected Topics in Signal Processing|volume = 12|issue = 4|pages = 806|last1 = Kamal|first1 = Mohasinina Binte|last2 = Mendis|first2 = Gihan J.|last3 = Wei|first3 = Jin|year = 2018}}</ref>▼
An '''adaptive neuro-fuzzy inference system''' or '''adaptive network-based fuzzy inference system''' ('''ANFIS''') is a kind of [[artificial neural network]] that is based on [[Fuzzy logic#Takagi–Sugeno–Kang (TSK)|Takagi–Sugeno fuzzy inference system]]. The technique was developed in the early 1990s.<ref>{{cite conference |last=Jang |first=Jyh-Shing R |title=Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm |year=1991|conference=Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, July 14–19 |volume=2 |pages=762–767 |url=http://www.aaai.org/Papers/AAAI/1991/AAAI91-119.pdf }}</ref><ref>{{cite journal |last=Jang |first=J.-S.R. |s2cid=14345934 |year=1993 |title=ANFIS: adaptive-network-based fuzzy inference system |journal=IEEE Transactions on Systems, Man, and Cybernetics |volume=23 |issue=3 |pages=665–685 |doi=10.1109/21.256541 }}</ref> Since it integrates both neural networks and [[fuzzy logic]] principles, it has potential to capture the benefits of both in a single [[:wikt:framework|framework]].
▲
==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 # 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==
Line 14 ⟶ 23:
[[Category:Fuzzy logic]]
[[Category:Artificial neural networks]]
| |||