Abstract
We show that standard feedforward neural networks with as few as a single hidden layer and arbitrary bounded nonlinear (continuous or noncontinuous) activation functions which have two unequal limits in infinities can uniformly approximate (in contrast to approximate measurably) arbitrary bounded continuous mappings on R/sup n/ with any precision. Especially, in a compact set of R/sup n/, standard feedforward neural networks with as few as a single hidden layer and arbitrary bounded nonlinear (continuous or noncontinuous) activation functions can uniformly approximate arbitrary continuous mappings with any precision. These results also hold for multi-hidden layer standard feedforward neural networks. We found that the boundedness and unequal limits at infinities conditions on the activation functions are sufficient, but not necessary.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
