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Reseach Article

Function Approximation using Neural and Fuzzy Methods

by Mithaq Nama Raheema, Ahmad Shaker Abdullah
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 3
Year of Publication: 2016
Authors: Mithaq Nama Raheema, Ahmad Shaker Abdullah

Mithaq Nama Raheema, Ahmad Shaker Abdullah . Function Approximation using Neural and Fuzzy Methods. Communications on Applied Electronics. 6, 3 ( Nov 2016), 35-42. DOI=10.5120/cae2016652427

@article{ 10.5120/cae2016652427,
author = { Mithaq Nama Raheema, Ahmad Shaker Abdullah },
title = { Function Approximation using Neural and Fuzzy Methods },
journal = { Communications on Applied Electronics },
issue_date = { Nov 2016 },
volume = { 6 },
number = { 3 },
month = { Nov },
year = { 2016 },
issn = { 2394-4714 },
pages = { 35-42 },
numpages = {9},
url = { },
doi = { 10.5120/cae2016652427 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:56:45.909450+05:30
%A Mithaq Nama Raheema
%A Ahmad Shaker Abdullah
%T Function Approximation using Neural and Fuzzy Methods
%J Communications on Applied Electronics
%@ 2394-4714
%V 6
%N 3
%P 35-42
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

This work deals with an approximation of functions which finds the underlying relationship from an available finite input-output data of the function. It is the fundamental problem in a majority of real world applications, such as signal processing, prediction, data mining and control system. In this paper five different methods are used to verify their efficiency of approximation: MLPNN, RBFNN, GRNN, FIS and ANFIS networks. The performance is compared by using the RMSE measurement as an indicator of the fitness of these models in function approximation problem. The experimental results show that the performance of all networks used in this work at the training process is more different at the checking process when the networks have been tested with unknown data points. This depends on many factors such as type of networks used to approximate the function, available training data, noise in the data and values of the required parameters for training each network (No. of layers, No. of neurons, No. of training epochs, etc.).

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Index Terms

Computer Science
Information Sciences


Function Approximation MLP GRNN RBFNN FIS ANFIS