Name
Title | ||
|---|---|---|
A generalized bipolar auto-associative memory model based on discrete recurrent neural networks |
Abstract | ||
|---|---|---|
This paper presents a novel method for designing associative memories based on discrete recurrent neural networks to accurately memorize the networks¿ external inputs. In the method, a generalized model is proposed for bipolar auto-associative memory and establishing an exponential stable criteria of the networks. The model is of generality with considering time delay and introducing a tunable slope activation function, and can robustly recall the memorized external input patterns in an auto-associative way. Experimental verification demonstrates that the proposed method is more effective and generalized than other existing ones. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2015.03.052 | Neurocomputing |
Keywords | Field | DocType |
Auto-associative memory,External inputs,Discrete recurrent neural networks,Slope activation function,Time delay | Autoassociative memory,Associative property,Exponential function,Pattern recognition,Activation function,Computer science,Recurrent neural network,Artificial intelligence,Recall,Memorization,Machine learning,Generality | Journal |
Volume | Issue | ISSN |
162 | C | 0925-2312 |
Citations | PageRank | References |
7 | 0.43 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caigen Zhou | 1 | 35 | 1.46 |
| Xiaoqin Zeng | 2 | 407 | 32.97 |
| Haibo Jiang | 3 | 12 | 1.55 |
| Lixin Han | 4 | 135 | 14.47 |