An enhanced swarm intelligence based training algorithm for RBF neural networks in function approximation

Salem, Mohammed; Zingla, Meriem Amina; Khelfi, Mohamed Fayçal

doi:10.1109/icocs.2014.7060917

Cited by 1 publication

(1 citation statement)

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“…From a finite data set, the basic task of a function approximation method is to find the suitable relationship between variables and their corresponding responses [4]. There are different approaches of the function approximation including analytical methods such as least squares linear approximation, polynomial approximation, and shape-preserving approximation in addition to many intelligent methods such as approximation with Fuzzy [5][6], Neural Networks (NNs) [7][8] or combined between neural and fuzzy [3,[9][10]. Both NNs and fuzzy logic can be recommended as universal function approximators, provided that sufficient hidden neurons in NN or rules in fuzzy logic [3] can give good performance for nonlinear function approximation.…”

Section: Introductionmentioning

confidence: 99%

Function Approximation using Neural and Fuzzy Methods

Nama¹,

Shaker²

2016

CAE

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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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