Name
Title | ||
|---|---|---|
Fast-Convergence Learning Algorithms for Multi-Level and Binary Neurons and Solution of Some Image Processing Problems |
Abstract | ||
|---|---|---|
In this paper we consider fast-convergence learning algorithms for multi-valued and universal binary neurons. These neurons are suggested to be used for the design of neural networks based on Cellular Neural Networks (CNN) —in the sense of connections between neurons. On the basis of such networks we offer a solution to some problems of image processing. For instance, a highly efficient method for contours distinguishing, obtained by the learning algorithm described in this paper is presented. |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1007/3-540-56798-4_152 | IWANN |
Keywords | Field | DocType |
fast-convergence learning algorithms,binary neurons,cellular neural network,neural network,image processing | Competitive learning,Computer science,Wake-sleep algorithm,Recurrent neural network,Time delay neural network,Artificial intelligence,Deep learning,Artificial neural network,Pattern recognition,Algorithm,Types of artificial neural networks,Cellular neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-56798-4 | 3 | 0.62 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naum N. Aizenberg | 1 | 54 | 7.83 |
| Igor N. Aizenberg | 2 | 306 | 31.31 |