DocumentCode :
3251390
Title :
Dynamical stability and parameter selection in neural optimization
Author :
Kamgar-Parsi, B. ; Kamgar-Parsi, B.
Author_Institution :
US Naval Res. Lab., Washington, DC, USA
Volume :
4
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
566
Abstract :
Finding suitable parameter values in solving optimization problems with the Hopfield net is of crucial importance. The authors present a systematic approach, based on analyzing dynamical stability of valid solutions for finding relationships among the parameters which make their selection much easier. This technique can show whether the problem formulation is flawed. As an example they discuss the Hopfield-Tank formulation of the traveling salesman problem and show that it is dynamically unstable, hence obtaining valid solutions is difficult. The modified formulation given by S.Y.B. Aiyer et al. (1990) which overcomes the instability problems is also discussed
Keywords :
Hopfield neural nets; optimisation; Hopfield-Tank formulation; dynamical stability; neural optimization; parameter selection; traveling salesman problem; valid solutions; Cost function; Differential equations; Government; Hopfield neural networks; Laboratories; Neural networks; Neurons; Protection; Stability analysis; Traveling salesman problems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.227259
Filename :
227259
Link To Document :
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