Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: The construction of a Boolean competitive neural network using ideas from immunology
Author: de Castro, LN
Von Zuben, FJ
de Deus, GA
Abstract: The immune system is capable of recognizing and responding to microorganisms and molecules that cannot be perceived by our sensory mechanisms, which send stimuli straight into the brain. It performs an accessory role for nervous cognition. This paper main goals are: (I) to show how some immune principles and theories can be used as sources of inspiration to develop novel neural network learning algorithms; (2) to survey the main works from the literature that employ the immune metaphor for the development of neural network architectures; and (3) to illustrate, with a new network model, how this source of inspiration can be actually used to develop a neural network learning algorithm. The novel learning algorithm proposed has the main features of competitive learning, automatic generation of the network structure and binary representation of the connection strengths (weights). The behavior of the algorithm is primarily described using a benchmark task, and some of its potential applications are illustrated using two simple real-world problems and a binary character recognition task. The results show that the network is a promising tool for solving problems that are inherently binary, and also that the immune system provides a new paradigm to search for neural network learning algorithms. (C) 2002 Elsevier Science B.V. All rights reserved.
Subject: immune system
clonal selection
competitive neural networks
pattern recognition
Country: Holanda
Editor: Elsevier Science Bv
Citation: Neurocomputing. Elsevier Science Bv, v. 50, n. 51, n. 85, 2003.
Rights: fechado
Date Issue: 2003
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
WOS000180567700005.pdf794.52 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.