Paper Info
Title
Recurrent Network Expression And Its Property Of Replicator Dynamics For Optimization
Abstract
Replicator dynamics (RD) is a well-known mathematical model of evolutionary dynamics. In the study of optimization, a gradient dynamics called the variable metric gradient projection (VMGP) model, which is used to solve a constrained optimization problem with normalized equality and nonnegative inequalities, is known to have the structure of RD.In this paper, we show that the VMGP dynamics can also be considered to have the structure of recurrent neural network (N.N.) by introducing a new variable so as to transform the VMGP dynamics equivalently. We found that it is described as a new model similar to the well known Hopfield's N.N. by regarding the newly introduced variable as "inner state" and giving a particular nonlinear element as output unit of the network. We also provide some interesting properties of the network model through fixed point analysis for the nonlinear dynamics. Numerical simulations show the validity of our discussions.
Year
DOI
Venue
2004
10.1109/ICSMC.2004.1400882
2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7
Keywords
Field
DocType
constrained optimization, replicator dynamics, recurrent network, nonlinear dynamical system, fixed point analysis
Nonlinear system,Computer science,Control theory,Replicator equation,Recurrent neural network,Nonlinear element,Artificial intelligence,Evolutionary dynamics,Fixed point,Network model,Machine learning,Constrained optimization
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Kazuaki Masuda174.21
Eitaro Aiyoshi25211.55