Title: Comparative study of fuzzy methods for response integration in ensemble neural networks
Authors: Miguel Lopez, Patricia Melin, Oscar Castillo
Addresses: Universidad Autonoma de Baja California, Tijuana, B.C., Mexico. ' Tijuana Institute of Technology, B.C., Mexico. ' Tijuana Institute of Technology, B.C., Mexico
Abstract: A new method for response integration in ensemble neural networks with Type-1 and Type-2 Fuzzy Logic is presented. Genetic Algorithms (GA|s) are used for fuzzy system optimisation. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints to test the proposed method of response integration. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on its biometric measure (Pattern recognition for fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules.
Keywords: ensemble neural networks; fuzzy logic; GAs; genetic algorithms; pattern recognition; response integration; optimisation; fingerprints.
International Journal of Advanced Intelligence Paradigms, 2009 Vol.1 No.3, pp.291 - 317
Published online: 19 Jun 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article