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=== Fuzzy logic ===
[[Fuzzy logic]] is an aspect of computing that handles approximate reasoning. Typically, [[Boolean algebra|binary logic]] allows computers to make decisions on true or false reasons (0s and 1s); however, introducing fuzzy logic allows systems to handle the unknowns between 0 and 1.<ref name="Procedia" /><ref>{{Cite web |date=2018-04-10 |title=Fuzzy Logic {{!}} Introduction |url=https://www.geeksforgeeks.org/fuzzy-logic-introduction/ |access-date=2023-11-11 |website=GeeksforGeeks |language=en-US}}</ref>
Unlike [[Classical set theory|classical sets]] that allow members to be entirely within or out, fuzzy sets allow partial membership by incorporating "graduation" between sets. Fuzzy logic operations include [[negation]], conjunction, and [[Logical disjunction|disjunction]], which handle membership between data sets.<ref name=":0" />
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=== Neural networks ===
[[Neural network]]s are computational models that attempt to mimic the structure and functioning of the human brain. While computers typically use [[Boolean algebra|binary logic]] to solve problems, neural networks attempt to provide solutions for complicated problems by enabling systems to think human-like, which is essential to soft computing.<ref name=":2">{{Cite web |title=Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges {{!}} IEEE Journals & Magazine {{!}} IEEE Xplore |url=https://ieeexplore.ieee.org/document/8253600/;jsessionid=Es-8JJ2aTxyDbz-ZeAW6ojB2bGom7NU413NP86MhLqTbzB3fmAGf!-668841979 |access-date=2023-11-11 |website=ieeexplore.ieee.org}}</ref>
Neural networks revolve around [[Perceptron|perceptrons]], which are [[Artificial neuron|artificial neurons]] structured in layers. Like the human brain, these interconnected nodes process information using complicated mathematical operations.<ref>{{Cite web |title=What are Neural Networks? {{!}} IBM |url=https://www.ibm.com/topics/neural-networks |access-date=2023-11-11 |website=www.ibm.com |language=en-us}}</ref>
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== References ==
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