Name
Abstract | ||
|---|---|---|
Kuremoto et al. proposed a multi-layer chaotic neural network (MCNN) combined multiple Adachi et al.' s CNNs to realize mutual auto-association of plural time series patterns. However, the MCNN was limited in a two-layer model. In this paper, we extend the MCNN to be a general form (GMCNN) with more layers and use particle swarm optimization (PSO) to improve the recollection performance of GMCNN. The recollecting characteristics by different parameter-control methods were investigated by computer simulations. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.2991/jrnal.2014.1.1.14 | JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE |
Keywords | Field | DocType |
chaotic neural network, association memory, time-series pattern, particle swarm optimization | Particle swarm optimization,Plural,Artificial intelligence,Chaotic neural network,Recall,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
1 | 1 | 2352-6386 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shun Watanabe | 1 | 84 | 15.05 |
| Takashi Kuremoto | 2 | 196 | 27.73 |
| Shingo Mabu | 3 | 493 | 77.00 |
| Masanao Obayashi | 4 | 198 | 26.10 |
| Kunikazu Kobayashi | 5 | 173 | 21.96 |