Soft computing: Difference between revisions

Revision as of 22:05, 11 December 2023

Revision as of 16:00, 12 December 2023

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= Soft computing =

'''Soft computing''' is an umbrella term used to describe types of [[algorithm]]s that produce approximate solutions to unsolvable high-level problems in computer science. Typically, traditional hard-computing algorithms heavily rely on concrete data and [[Mathematical Models and Methods in Applied Sciences|mathematical models]] to produce solutions to problems. Soft computing was coined in the late 20th century.<ref>{{Cite journal |last=Zadeh |first=Lotfi A. |date=March 1994 |title=Fuzzy logic, neural networks, and soft computing |url=https://dl.acm.org/doi/10.1145/175247.175255 |journal=Communications of the ACM |language=en |volume=37 |issue=3 |pages=77–84 |doi=10.1145/175247.175255 |issn=0001-0782}}</ref> During this period, revolutionary research in three fields greatly impacted soft computing. Fuzzy logic is a computational paradigm that entertains the uncertainties in data by using levels of truth rather than rigid 0s and 1s in binary. Next, neural networks which are computational models influenced by human brain functions. Finally, evolutionary computation is a term to describe groups of algorithm that mimic natural processes such as evolution and natural selection.

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== Computational techniques ==

=== Fuzzy Logiclogic ===

[[Fuzzy logic]] is an aspect of computing that handles approximate reasoning. Typically, [[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|Neural networks]]s are computational models that attempt to mimic the structure and functioning of the human brain. While computers typically use [[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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In soft computing, evolutionary computation helps applications of [[data mining]] (using large sets of data to find patterns), [[robotics]], optimizing, and engineering methods.<ref name=":1" /><ref name=":0" />

=== Hybrid Intelligenceintelligence Systemssystems ===

Hybrid intelligence systems combine the strengths of soft computing components to create integrated computational models. Artificial techniques such as fuzzy logic, neural networks, and evolutionary computation combine to solve problems efficiently. These systems improve judgment, [[troubleshooting]], and [[data analysis]]. Hybrid intelligence systems help overcome the limitations of individual AI approaches to improve performance, accuracy, and adaptability to address [[Dynamic problem (algorithms)|dynamic problems]]. It advances soft computing capabilities in data analysis, pattern recognition, and systems.<ref name=":4">{{Cite book |last=Medsker |first=Larry R. |url=https://books.google.com/books?id=EXngBwAAQBAJ&lpg=PR13&ots=GgqzFWNqj0&dq=hybrid%20intelligent%20system&lr&pg=PR13#v=snippet&q=evolutionary&f=false |title=Hybrid Intelligent Systems |date=2012-12-06 |publisher=Springer Science & Business Media |isbn=978-1-4615-2353-6 |language=en}}</ref>

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Furthermore, there needs to be more backing behind soft computing algorithms, which makes them less reliable than complicated computing models. Finally, there is a considerable potential for bias because of the input data, which leads to ethical dilemmas if methods are in fields such as medicine, finance, and healthcare.

== References ==

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{{Reflist}}

= [[Category:Soft computing| =]]

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[[Category:Soft computing]]

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