Name
Abstract | ||
|---|---|---|
This book contains a wide variety of hot topics on advanced computational intelligence methods which incorporate the concept of complex and hypercomplex number systems into the framework of artificial neural networks. In most chapters, the theoretical descriptions of the methodology and its applications to engineering problems are excellently balanced. This book suggests that a better information processing method could be brought about by selecting a more appropriate information representation scheme for specific problems, not only in artificial neural networks but also in other computational intelligence frameworks. The advantages of CVNNs and hypercomplex-valued neural networks over real-valued neural networks are confirmed in some case studies but still unclear in general. Hence, there is a need to further explore the difference between them from the viewpoint of nonlinear dynamical systems. Nevertheless, it seems that the applications of CVNNs and hypercomplex-valued neural networks are very promising. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MCI.2013.2247895 | IEEE Computational Intelligence Magazine |
Keywords | Field | DocType |
neural networks,artificial neural networks | Nervous system network models,Information processing,Computational intelligence,Computer science,Hypercomplex number,Nonlinear dynamical systems,Artificial intelligence,Artificial neural network,Machine learning,Information representation | Journal |
Volume | Issue | ISSN |
8 | 2 | 1556-603X |
Citations | PageRank | References |
2 | 0.39 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gouhei Tanaka | 1 | 51 | 11.80 |