Loop neural network model for associative memory

Author

Miao Zhenjiang ; Yuan Baozong

Author_Institution

Inst. of Inf. Sci., Northern Jiaotong Univ., Beijing, China

Volume

2

fYear

1993

fDate

19-21 Oct. 1993

Firstpage

758

Abstract

Proposes a new associative memory neural net (NN) model called the loop neural network model, and the theoretical proof of this NN´s stability is given. Experiments show that this NN model is much more powerful than the McCulloch-Pitts model, the discrete Hopfield NN, the continuous Hopfield NN, the discrete bidirectional associative memory NN, the continuous and adaptive bidirectional associative memory NN, the backpropagation NN, and the optimally designed nonlinear continuous NN.<>

Keywords

content-addressable storage; neural nets; stability; McCulloch-Pitts model; backpropagation neural net; continuous Hopfield neural net; continuous adaptive bidirectional associative memory neural net; discrete Hopfield neural net; discrete bidirectional associative memory neural net; loop neural network model; optimally designed nonlinear continuous neural net; stability; Artificial neural networks; Associative memory; Differential equations; Hopfield neural networks; Information science; Learning; Multi-layer neural network; Neural networks; Neurons; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on

Conference_Location

Beijing, China

Print_ISBN

0-7803-1233-3

Type

conf

DOI

10.1109/TENCON.1993.320076

Filename

320076