Recurrent network expression and its property of replicator dynamics for optimization

Kazuaki Masuda, Eitaro Aiyoshi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
ページ3488-3493
ページ数6
DOI
出版ステータスPublished - 2004 12 1
イベント2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands
継続期間: 2004 10 102004 10 13

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
4
ISSN(印刷版)1062-922X

Other

Other2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
国/地域Netherlands
CityThe Hague
Period04/10/1004/10/13

ASJC Scopus subject areas

  • 工学(全般)

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