Soft computing: Difference between revisions

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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 webjournal |title=Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges |url=https://ieeexplore.ieee.org/document/8253600/;jsessionid=Es-8JJ2aTxyDbz-ZeAW6ojB2bGom7NU413NP86MhLqTbzB3fmAGf!-668841979 |access-date=2023-11-11 |websitejournal=ieeexploreIEEE Signal Processing Magazine| date=2018 | doi=10.ieee1109/MSP.org2017.2765695 | last1=Cheng | first1=Yu | last2=Wang | first2=Duo | last3=Zhou | first3=Pan | last4=Zhang | first4=Tao | volume=35 | issue=1 | pages=126–136 | bibcode=2018ISPM...35a.126C }}</ref>
 
Neural networks revolve around [[perceptron]]s, which are [[artificial neuron]]s 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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Through training, the network handles input and output data streams and adjusts parameters according to the provided information. Neural networks help make soft computing extraordinarily flexible and capable of handling high-level problems.
 
In soft computing, neural networks aid in pattern recognition, predictive modeling, and data analysis. They are also used in [[image recognition]], [[natural language processing]], [[speech recognition]], and [[system]]s.<ref name=":1" /><ref name=":3">{{Cite web |title=IEEE Xplore Full-Text PDF |url=https://ieeexplore.ieee.org/document/8859190 |access-date=2023-11-11 |websitedoi=ieeexplore10.ieee1109/ACCESS.org2019.2945545 }}</ref>
 
=== Evolutionary computation ===
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Due to their dynamic versatility, soft computing models are precious tools that confront complex real-world problems. They are applicable in numerous industries and research fields:
 
Soft computing fuzzy logic and neural networks help with pattern recognition, image processing, and computer vision. Its versatility is vital in [[natural language processing]] as it helps decipher human emotions and language. They also aid in data mining and [[Predictive analytics|predictive analysis]] by obtaining priceless insights from enormous datasets. Soft computing helps optimize solutions from energy, [[financial forecast]]s, environmental and biological data modeling, and anything that deals with or requires models.<ref name=":4" /><ref>{{Cite webjournal |title=Industrial applications of soft computing: a review |url=https://ieeexplore.ieee.org/document/949483/;jsessionid=xdS8IFFQN8YRhXQajnUBK1GxF5Fzj_edYcUsqEW5vE3xWwb3XJ8G!-1911429853 |access-date=2023-11-11 |websitejournal=ieeexploreProceedings of the IEEE| date=2001 | doi=10.ieee1109/5.org949483 | last1=Dote | first1=Y. | last2=Ovaska | first2=S.J. | volume=89 | issue=9 | pages=1243–1265 }}</ref>
 
Within the medical field, soft computing is revolutionizing disease detection, creating plans to treat patients and models of [[Health care|healthcare]].<ref name=":3" />