Recurrent correlation associative memories with multiple-value

Author

Chen, Zhong-Yu ; Kwong, Chung-Ping

Author_Institution

Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

2

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

1068

Abstract

We propose a modified multiple-valued recurrent correlation associative memory with exponential weighting function (MEMRCAM) which is a network model with high storage capacity and strong error correction capability. The operations of this kind of networks depend on the similarity-measure computation and the input-output relation of each neuron being step function. This new model adopts the same principle as that proposed in the previous work (Chiueh and Goodman, 1991). We show the convergent property of the MEMRCAM and simulation result verifies the strong error correction capability with large storage capacity of the MEMRCAM

Keywords

content-addressable storage; convergence; error correction; recurrent neural nets; MEMRCAM; convergent property; error correction capability; exponential weighting function; high storage capacity; input-output relation; large storage capacity; multiple-valued recurrent correlation associative memory; network model; recurrent correlation associative memories; similarity-measure computation; simulation result; step function; strong error correction; Associative memory; Convergence; Electronic mail; Error analysis; Error correction; Image processing; Neural networks; Neurons; Nonlinear equations; Prototypes;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374331

Filename

374331