A possibilistic approach to RBFN centers initialization

Authors:
A. Guillén;I. Rojas;J. González;H. Pomares;L. J. Herrera;O. Valenzuela;A. Prieto
Affiliations:
Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

Citing 8
Cited 6

Universal approximation using radial-basis-function networks

Neural Computation
A global learing algorithm for a RBF network

Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms

Clustering Algorithms
The enhanced LBG algorithm

Neural Networks
Comments on “A possibilistic approach to clustering”

IEEE Transactions on Fuzzy Systems
Improved possibilistic C-means clustering algorithms

IEEE Transactions on Fuzzy Systems
Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques

IEEE Transactions on Neural Networks

Boosting the Performance of a Multiobjective Algorithm to Design RBFNNs Through Parallelization

ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A First Approach to Birth Weight Prediction Using RBFNNs

IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Parallel multi-objective memetic RBFNNs design and feature selection for function approximation problems

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Training RBF neural networks with PSO and improved subtractive clustering algorithms

ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Multiobjective RBFNNs designer for function approximation: an application for mineral reduction

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
A fuzzy-possibilistic fuzzy ruled clustering algorithm for RBFNNs design

RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clustering techniques have always been oriented to solve classification and pattern recognition problems. This clustering techniques have been used also to initialize the centers of the Radial Basis Function (RBF) when designing an RBF Neural Network (RBFNN) that approximates a function. Since classification and function approximation problems are quite different, it is necessary to design a new clustering technique specialized in the problem of function approximation. In this paper, a new clustering technique it is proposed to make the right initialization of the centers of the RBFs. The novelty of the algorithm is the employment of a possibilistic partition of the data, rather than a hard or fuzzy partition as it is commonly used in clustering algorithms. The use of this kind of partition with the addition of several components to use the information provided by the output, allow the new algorithm to provide better results and be more robust than the other clustering algorithms even if noise exits in the input data.