Adaptation from fixed weight dynamic networks
- 24 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 155-160 vol.1
- https://doi.org/10.1109/icnn.1996.548883
Abstract
A characteristic often attributed to intelligent systems is adaptive behavior. For the purposes of this paper, we define adaptation as a system's ability to recognize change through its sensed inputs and to appropriately adjust its behavior in response to the perceived change. This paper explores the notion that a time-lagged recurrent network architecture can be made to exhibit adaptive behavior after network training has been completed, i.e., to exhibit adaptation after its weights have been fixed and without any external mechanism to control its behavior.Keywords
This publication has 6 references indexed in Scilit:
- Training controllers for robustness: multi-stream DEKFPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Learning algorithms and fixed dynamicsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Understanding Nonlinear DynamicsPublished by Springer Nature ,1995
- Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networksIEEE Transactions on Neural Networks, 1994
- Dynamics and BifurcationsPublished by Springer Nature ,1991
- Fixed-weight networks can learnPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990