CFP last date
01 April 2024
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
10.5120/cae2016652427

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 = { https://www.caeaccess.org/archives/volume6/number3/684-2016652427/ },
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
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.).

References
  1. Peter Andras, "Function Approximation Using Combined Unsupervised and Supervised Learning," IEEE, vol. 25, March 2014.
  2. Zarita Zainuddin, Ong Pauline, "Function approximation using artificial neural networks," International Journal of Systems Applications, Engineering & Development vol. 1, no. 4, 2007.
  3. Horng-Lin Shieh, Ying-Kuei Yang, Po-Lun Chang, Jin-Tsong Jeng, "Robust neural-fuzzy method for function approximation," Elsevier Ltd, p. 6903–6913, 2008.
  4. YunfengWu, "Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression," springer, 2014.
  5. Zarita Zainuddin, Pauline Ong, "Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation," springer, 29 January 2013.
  6. Julie A. Dickerson, Bart Kosko, "Fuzzy Function Approximation with Ellipsoidal Rules," IEEE, vol. 26, pp. 542-560, August 1996.
  7. Salim Heddam, Abdelmalek Bermad, Noureddine Dechemi, "Applications of Radial-Basis Function and Generalized Regression Neural Networks for Modeling of Coagulant Dosage in a Drinking Water-Treatment Plant: Comparative Study," Environment Engineering, December 2011.
  8. Mohammed Salem, Meriem Amina Zingla, Mohamed Faycal Khelfi, "An Enhanced Swarm Intelligence based Training Algorithm for RBF Neural Networks in Function Approximation," IEEE, 2014.
  9. Omid Khayat, Javad Razjouyan , Fereidoun Nowshiravan Rahatabad,Hadi Chahkandi Nejad, "A fast learnt fuzzy neural network for huge scale discrete data function approximationand prediction," Intelligent and fuzzy systems, May 2013.
  10. Andre Lemos, Vladik Kreinovich, Walmir Caminhas, Fernando Gomide, "Universal Approximation with Uninorm-Based Fuzzy Neural Networks," IEEE, 2011.
  11. Ramandeep Kaur, Prince Verma, "Improved MLP-NN based approach for Lung Diseases Classification," International Journal of Computer Applications, vol. 131, December2015.
  12. Gurpreet Singh, Manoj Sachan, "Multi-Layer Perceptron (MLP) Neural Network Technique for Offline Handwritten Gurmukhi Character Recognition," IEEE International Conference on Computational Intelligence and Computing Research, 2014.
  13. Nicolaos Karayiannis, "On the Construction and Training of Reformulated Radial Basis Function Neural Networks," IEEE, vol. 14, pp. 835-846, JULY 2003.
  14. Kah Wai Cheah, Noor Atinah Ahmad, "Universal Approximation of Reduced Fuzzy Basis Function With Ruspini Partitioning," ResearchGate, October 2015.
  15. Hung-Ching Lu, Sing-Fu Lee, "Function Approximation of Nonlinear Functions by GA-Based Fuzzy Systems," IEEE, July 2012.
  16. Jose Kleton, Nonlinear system idenification based on modified ANFIS, 2003.
  17. Lakhmi C. Jain; N.M. Martin, Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications, 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Function Approximation MLP GRNN RBFNN FIS ANFIS