RBF Centers Initialization Using Fuzzy Clustering Technique for Function Approximation Problems

Perales, Alberto Guillén; Ruiz, Ignacio Rojas; Peñalver, Jesús González; Pomares, H.; Herrera, José Benavente

doi:10.25102/fer.2005.02.03

Cited by 2 publications

(2 citation statements)

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“…Several algorithms have been designed specifically for this task, but they all were inspired in previous clustering algorithms [19,18,23,29]. This motivated the design of the algorithm proposed in this paper.…”

Section: Statement Of the Problemmentioning

confidence: 98%

Output value-based initialization for radial basis function neural networks

Guillén

Rojas

González

et al. 2007

Neural Process Lett

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The use of Radial Basis Function Neural Networks (RBFNNs) to solve functional approximation problems has been addressed many times in the literature. When designing an RBFNN to approximate a function, the first step consists of the initialization of the centers of the RBFs. This initialization task is very important because the rest of the steps are based on the positions of the centers. Many clustering techniques have been applied for this purpose achieving good results although they were constrained to the clustering problem. The next step of the design of an RBFNN, which is also very important, is the initialization of the radii for each RBF. There are few heuristics that are used for this problem and none of them use the information provided by the output of the function, but only the centers or the input vectors positions are considered. In this paper, a new algorithm to initialize the centers and the radii of an RBFNN is proposed. This algorithm uses the perspective of activation grades for each neuron, placing the centers according to the output of the target function. The radii are initialized using the center's positions and their activation grades so the calculation of the radii also uses the information provided by the output of the target function. As the experiments show, the performance of the new algorithm outperforms other algorithms previously used for this problem.

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Section: Statement Of the Problemmentioning

confidence: 98%

Output value-based initialization for radial basis function neural networks

Guillén

Rojas

González

et al. 2007

Neural Process Lett

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several algorithms have been designed specifically for this task, but all of them were inspired in previous clustering algorithms [18], [19], [23], [29]. This motivated the design of the algorithm proposed in this paper from other perspective.…”

Section: Introductionmentioning

confidence: 97%

Supervised RBFNN Centers and Radii Initialization for Function Approximation Problems

Guillén¹,

Rojas²,

González³

et al. 2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Radial Basis Function Neural Networks (RBFNNs) have been applied to solve problems of classification, function approximation and time series prediction. In the design of an RBFNN it is necessary to set the values for the positions of the centers and the radii for each RBF. In the literature it is usually performed an initialization step to set the positions of the centers and, once they are placed, the radii are calculated using a heuristic. In this paper, a new algorithm to set the value of those two parameters is presented. This new algorithm uses a supervised learning in such a way that the position of the centers will be constrained by the output of the function to be approximated. Since each center represents a neuron that is activated by the input vectors, the radii are initialized using the center's positions and their activation grades. In this way, the calculation of the radii is also influenced by the output of the target function, not like in other heuristics where only the positions of the centers or the input vectors are considered. As the experiments show, the new algorithm outperforms other algorithms previously used for this problem.

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RBF Centers Initialization Using Fuzzy Clustering Technique for Function Approximation Problems

Cited by 2 publications

References 0 publications

Output value-based initialization for radial basis function neural networks

Output value-based initialization for radial basis function neural networks

Supervised RBFNN Centers and Radii Initialization for Function Approximation Problems

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