Theory of Interacting Neural Networks
| dc.creator | Kinzel, Wolfgang | |
| dc.date | 2002-04-02 | |
| dc.date.accessioned | 2026-07-07T02:44:53Z | |
| dc.date.available | 2026-07-07T02:44:53Z | |
| dc.description | In this contribution we give an overview over recent work on the theory of interacting neural networks. The model is defined in Section 2. The typical teacher/student scenario is considered in Section 3. A static teacher network is presenting training examples for an adaptive student network. In the case of multilayer networks, the student shows a transition from a symmetric state to specialisation. Neural networks can also generate a time series. Training on time series and predicting it are studied in Section 4. When a network is trained on its own output, it is interacting with itself. Such a scenario has implications on the theory of prediction algorithms, as discussed in Section 5. When a system of networks is trained on its minority decisions, it may be considered as a model for competition in closed markets, see Section 6. In Section 7 we consider two mutually interacting networks. A novel phenomenon is observed: synchronisation by mutual learning. In Section 8 it is shown, how this phenomenon can be applied to cryptography: Generation of a secret key over a public channel. | |
| dc.description | Contribution to Networks, ed. by H.G. Schuster and S. Bornholdt, to be published by Wiley VCH | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0204054 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0204054 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/19111 | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.title | Theory of Interacting Neural Networks | |
| dc.type | text |