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Selecting artificial neural network ...

Bliss, Lawrence Allen.

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Selecting artificial neural network inputs using particle swarm optimization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Selecting artificial neural network inputs using particle swarm optimization./
Author:	Bliss, Lawrence Allen.
Description:	229 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4450.
Contained By:	Dissertation Abstracts International64-09B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3106822

Selecting artificial neural network inputs using particle swarm optimization.
Bliss, Lawrence Allen.

Selecting artificial neural network inputs using particle swarm optimization. - 229 p.

Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4450.

Thesis (D.P.S.)--Pace University, 2003.

Much work has been clone in the area of configuring Artificial Neural Network (ANN) topology automatically using soft computing techniques such as Genetic Algorithms (GA). However, little time has been spent by researches on selecting the proper inputs to the ANN. Neural Networks used to predict the behavior of Dynamical Systems often have a choice of input information, much of which is redundant. Selecting a minimal set of inputs that produce acceptable behavior results in a lower cost, solution. Researchers using trial and error methods currently do this input selection manually. The work presented in this paper shows several methods of automatically selecting a small set of inputs from a large candidate population. Particle Swarm Optimization is the primary network optimization technique used for processing the ANN configuration.Subjects--Topical Terms:

626642
Computer Science.

Selecting artificial neural network inputs using particle swarm optimization.
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100 1 $a Bliss, Lawrence Allen. $3 1942119
245 1 0 $a Selecting artificial neural network inputs using particle swarm optimization.
300 $a 229 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4450.
500 $a Adviser: Michael Gargano.
502 $a Thesis (D.P.S.)--Pace University, 2003.
520 $a Much work has been clone in the area of configuring Artificial Neural Network (ANN) topology automatically using soft computing techniques such as Genetic Algorithms (GA). However, little time has been spent by researches on selecting the proper inputs to the ANN. Neural Networks used to predict the behavior of Dynamical Systems often have a choice of input information, much of which is redundant. Selecting a minimal set of inputs that produce acceptable behavior results in a lower cost, solution. Researchers using trial and error methods currently do this input selection manually. The work presented in this paper shows several methods of automatically selecting a small set of inputs from a large candidate population. Particle Swarm Optimization is the primary network optimization technique used for processing the ANN configuration.
590 $a School code: 0483.
650 4 $a Computer Science. $3 626642
650 4 $a Physics, Fluid and Plasma. $3 1018402
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710 2 0 $a Pace University. $3 1024552
773 0 $t Dissertation Abstracts International $g 64-09B.
790 1 0 $a Gargano, Michael, $e advisor
790 $a 0483
791 $a D.P.S.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3106822