Title :
Locally recurrent globally feedforward networks: a critical review of architectures
Author :
Tsoi, Ah Chung ; Back, Andrew D.
Author_Institution :
Dept. of Electr. & Comput. Eng., Queensland Univ., St. Lucia, Qld., Australia
fDate :
3/1/1994 12:00:00 AM
Abstract :
In this paper, we will consider a number of local-recurrent-global-feedforward (LRGF) networks that have been introduced by a number of research groups in the past few years. We first analyze the various architectures, with a view to highlighting their differences. Then we introduce a general LRGF network structure that includes most of the network architectures that have been proposed to date. Finally we will indicate some open issues concerning these types of networks
Keywords :
feedforward neural nets; recurrent neural nets; locally recurrent globally feedforward neural networks; Artificial neural networks; Australia Council; Control systems; Delay; Finite impulse response filter; Input variables; Multilayer perceptrons; Neurofeedback; Neurons; Transfer functions;
Journal_Title :
Neural Networks, IEEE Transactions on