000			01982naa a2200241 a 4500
003			AR-LpUFIB
005			20250311170426.0
008			230201s2004 xx o 000 0 eng d
024	8		_aDIF-M6860 _b6995 _zDIF006265
040			_aAR-LpUFIB _bspa _cAR-LpUFIB
100	1		_aCorbalán, Leonardo César
245	1	0	_aALENA : _badaptive-length evolving neural arrays
300			_a1 archivo (164,7 KB)
500			_aFormato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
520			_aEvolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as robotics and process control. Unlike conventional methods -based on a single neural network- ENAs are made up of a set of networks organized as an array. Each of them represents a part of the expected solution. This work describes a new method, ALENA, that enhances the solutions obtained by solving the main deficiencies of ENA since it eases the obtaining of specialized components, does not require the explicit decomposition of the problem into subtasks, and is capable of automatically adjusting the arrays length for each particular use. The measurements of the proposed method -applied to problems of obstacle evasion and objects collection- show the superiority of ALENA in relation to the traditional methods that deal with populations of neural networks. SANE has been used in particular as a comparative referent due to its high performance. Eventually, conclusions and some future lines of work are presented.
534			_aJournal of Computer Science & Technology, 4(1), pp. 59-65
650		4	_aREDES NEURONALES
650		4	_aALGORITMOS GENÉTICOS
700	1		_aLanzarini, Laura Cristina
700	1		_aDe Giusti, Armando Eduardo
856	4	0	_uhttp://goo.gl/nBsq60
942			_cCP
999			_c56046 _d56046