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[[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 journal |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 |journal=IEEE Signal Processing Magazine| date=2018 | doi=10.1109/MSP.2017.2765695 | 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 |date=6 October 2021 |language=en-us}}</ref>
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 journal |title=Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition
=== Evolutionary computation ===
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