Title :
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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374331
