Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms: The Case of Face, Fingerprint and Voice Recognition

Studies in computational intelligencePublished 1 January 2009

Ricardo Conejo, Oscar Castillo, Patricia Melín

DOI10.1007/978-3-642-04514-1_7

Source URL

Citations7

SJR quartileQ4

SJR score0.19

SNIP0.29

TL;DR

It is shown that using a HGA as an optimization technique for the fuzzy integrators is a good option to solve MNN integration problems.

Abstract

In this paper we describe the development of Fuzzy Response Integrators of Modular Neural Networks (MNN) for Face, Fingerprint and Voice Recognition, and their Optimization with a Hierarchical Genetic Algorithm (HGA). The optimization of the integrators consists of optimizing their membership functions, fuzzy rules, type of model (Mamdani or Sugeno), type of fuzzy logic (type-1 or type-2). The MNN architecture consists of three modules; face, fingerprint and voice. Each of the modules is divided again into three sub modules. The same information is used as input to train the sub modules. Once we have trained and tested the MNN modules, we proceed to integrate these modules with an optimized fuzzy integrator. In this paper we show that using a HGA as an optimization technique for the fuzzy integrators is a good option to solve MNN integration problems.

Keywords

Computer Science

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