Welcome to the InfoSci Platform
Reference Hub6
Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria

Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria

Daniela Danciu
Copyright: © 2009 |Pages: 27
ISBN13: 9781599049960|ISBN10: 1599049961|ISBN13 Softcover: 9781616925376|EISBN13: 9781599049977
DOI: 10.4018/978-1-59904-996-0.ch018
Cite Chapter Cite Chapter

MLA

Danciu, Daniela. "Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria." Advancing Artificial Intelligence through Biological Process Applications, edited by Ana B. Porto Pazos, et al., IGI Global, 2009, pp. 331-357. https://doi.org/10.4018/978-1-59904-996-0.ch018

APA

Danciu, D. (2009). Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria. In A. Porto Pazos, A. Pazos Sierra, & W. Buño Buceta (Eds.), Advancing Artificial Intelligence through Biological Process Applications (pp. 331-357). IGI Global. https://doi.org/10.4018/978-1-59904-996-0.ch018

Chicago

Danciu, Daniela. "Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria." In Advancing Artificial Intelligence through Biological Process Applications, edited by Ana B. Porto Pazos, Alejandro Pazos Sierra, and Washington Buño Buceta, 331-357. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-996-0.ch018

Export Reference

Mendeley
Favorite

Abstract

Neural networks—both natural and artificial, are characterized by two kinds of dynamics. The first one is concerned with what we would call “learning dynamics”. The second one is the intrinsic dynamics of the neural network viewed as a dynamical system after the weights have been established via learning. The chapter deals with the second kind of dynamics. More precisely, since the emergent computational capabilities of a recurrent neural network can be achieved provided it has suitable dynamical properties when viewed as a system with several equilibria, the chapter deals with those qualitative properties connected to the achievement of such dynamical properties as global asymptotics and gradient-like behavior. In the case of the neural networks with delays, these aspects are reformulated in accordance with the state of the art of the theory of time delay dynamical systems.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.