@Article{ATA-39-1,
author = {Yu, Dansheng and Yunyou, Qian and Li, Fengjun},
title = {On Approximation by Neural Networks with Optimized Activation Functions and Fixed Weights},
journal = {Analysis in Theory and Applications},
year = {2023},
volume = {39},
number = {1},
pages = {93--104},
abstract = {<p style="text-align: justify;">Recently, Li [16] introduced three kinds of single-hidden layer feed-forward
neural networks with optimized piecewise linear activation functions and fixed
weights, and obtained the upper and lower bound estimations on the approximation
accuracy of the FNNs, for continuous function defined on bounded intervals. In the
present paper, we point out that there are some errors both in the definitions of the
FNNs and in the proof of the upper estimations in [16]. By using new methods, we
also give right approximation rate estimations of the approximation by Li’s neural
networks.</p>},
issn = {1573-8175},
doi = {https://doi.org/10.4208/ata.OA-2021-0006},
url = {https://global-sci.com/article/73728/on-approximation-by-neural-networks-with-optimized-activation-functions-and-fixed-weights}
}