by
Department of Applied Mathematics
The Hong Kong Polytechnic University
Hung Hom, Kowloon, Hong Kong
Email: malblkli@polyu.edu.hk
Abstract :
In this talk, we introduce the mathematical model for leaky integrator
recurrent neural networks. For a given trajectory, we may use a recurrent neural
network dynamic to approximate the trajectory. Learning is a process for which
we adjust the neural parameters so that the network output comes close to the
trajectory. This process of modifying parameters is called "learning". Williams
and Zipser (1989) derived the learning algorithms for trajectories of a
discrete-time recurrent neural network but the question of the capability of the
recurrent network is still unknown. Then, we consider a learning algorithm for
discrete-time recurrent neural networks as a nonlinear numerical optimization
problem. We shall also discuss the relations between least square errors and the
network sizes. We also compare our results with linear time series models.