Content deleted Content added
Wikishovel (talk | contribs) |
m Open access bot: doi updated in citation with #oabot. |
||
Line 1:
'''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|doi-access=free }}</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.
In the context of [[artificial intelligence]] and [[machine learning]], soft computing provides tools to handle real-world uncertainties. Its methods supplement preexisting methods for better solutions. Today, the combination with artificial intelligence has led to hybrid intelligence systems that merge various computational algorithms. Expanding the applications of artificial intelligence, soft computing leads to robust solutions. Key points include tackling ambiguity, flexible learning, grasping intricate data, real-world applications, and [[Ethics of artificial intelligence|ethical artificial intelligence]].<ref name="Procedia">Ibrahim, Dogan. "An overview of soft computing." Procedia Computer Science 102 (2016): 34-38.</ref><ref name=":1">{{Cite book |last=Kecman |first=Vojislav |url=https://books.google.com/books?id=W5SAhUqBVYoC&lpg=PP21&ots=ev4aPrdxZ4&dq=neural%20networks%20in%20soft%20computing&lr&pg=PP1#v=onepage&q=neural%20networks%20in%20soft%20computing&f=false |title=Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models |date=2001 |publisher=MIT Press |isbn=978-0-262-11255-0 |language=en}}</ref>
| |||