On Approximation by Neural Networks with Optimized Activation Functions and Fixed Weights (Analysis in Theory and Applications, 2023)
Recently, Li [16] introduced three kinds of single-hidden layer feed-forwardneural networks with optimized piecewise linear activation functions and fixedweights, and obtained the upper and lower bound estimations on the approximationaccuracy of the FNNs, for continuous function defined on bounded intervals. In thepresent paper, we point out that there are some errors both in the definitions of theFNNs and in the proof of the upper estimations in [16]. By using new methods, wealso give right approximation rate estimations of the approximation by Li’s neuralnetworks.