Function approximation, a central theme in numerical analysis and applied mathematics, seeks to represent complex functions through simpler or more computationally tractable forms. In this context, ...

At least implicitly, functions are the daily concern of most engineers and scientists. When they are not very smooth, i.e. when they do not have a significant number of derivatives, they can be ...

Abstract: Function approximation has experienced significant success in the field of reinforcement learning (RL). Despite a handful of progress on developing theory for nonstationary RL with function ...

Resistant training in radial basis function (RBF) networks is the topic of this paper. In this paper, one modification of Gauss-Newton training algorithm based on the theory of robust regression for ...

One of the issues raised in mathematical and engineering sciences is the ap proximation of a function. Function approximation means that, by having the ability to calculate the value of an unknown ...

In this paper, a hybrid Fuzzy Neural Network (FNN) system for function approximation is presented. The proposed FNN can handle numeric and fuzzy inputs simultaneously. The numeric inputs are fuzzified ...