Control of chaotic neural networks based on contraction mappings
β Scribed by Hongjie Yu; Yanzhu Liu; Jianhua Peng
- Publisher
- Elsevier Science
- Year
- 2004
- Tongue
- English
- Weight
- 336 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0960-0779
No coin nor oath required. For personal study only.
β¦ Synopsis
The control chaos method based on contraction mappings is applied to small discrete neural networks for stabilizing a desired periodic orbit embedded in the chaotic attractor by a external input. The suggested method utilizes only an approximate location of the desired periodic orbit, one of the merits of this method is that the linearization of the system near the stabilized orbit is not required. Typical examples of two-and three-dimensional neural networks are given, and the validity of the method is shown by the numerical simulation.
π SIMILAR VOLUMES
In this paper, we investigate the synchronization problems of chaotic fuzzy cellular neural networks with time-varying delays. To overcome the difficulty that complete synchronization between non-identical chaotic neural networks cannot be achieved only by utilizing output feedback control, we use a
In this paper, we prove that any continuous mapping can be approximately realized by Rumelhart-Hinton-Williams' multilayer neural networks with at least one hidden layer whose output functions are sigmoid functions. The starting point of the proof for the one hidden layer case is an integral formula