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Function Approximation using Neural and Fuzzy Methods

Mithaq Nama Raheema, Ahmad Shaker Abdullah. Published in Fuzzy Systems.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Mithaq Nama Raheema, Ahmad Shaker Abdullah
10.5120/cae2016652427

Mithaq Nama Raheema and Ahmad Shaker Abdullah. Function Approximation using Neural and Fuzzy Methods. Communications on Applied Electronics 6(3):35-42, November 2016. BibTeX

@article{10.5120/cae2016652427,
	author = {Mithaq Nama Raheema and Ahmad Shaker Abdullah},
	title = {Function Approximation using Neural and Fuzzy Methods},
	journal = {Communications on Applied Electronics},
	issue_date = {November 2016},
	volume = {6},
	number = {3},
	month = {Nov},
	year = {2016},
	issn = {2394-4714},
	pages = {35-42},
	numpages = {8},
	url = {http://www.caeaccess.org/archives/volume6/number3/684-2016652427},
	doi = {10.5120/cae2016652427},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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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Keywords

Function Approximation, MLP, GRNN, RBFNN, FIS, ANFIS