Theory of interacting neural networks

Kinzel, Wolfgang

doi:10.1002/3527602755.ch9

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…Neural cryptography Kinzel 2002, Kinzel 2002) is based on the effect that two neural networks are able to synchronize by mutual learning (Ruttor et al 2006). In each step of this online procedure they receive a common input pattern and calculate their output.…”

Section: Neural Cryptographymentioning

confidence: 99%

“…Two partners A and B want to exchange a secret message over a public channel. In order to protect the content against an attacker T, who is listening to the communication, A encrypts the message, but B needs A's secret key over the public channel (Kinzel 2002). This can be achieved by synchronizing two TPMs (Three Parity Machines), one for A and one for B, respectively.…”

Section: Neural Cryptographymentioning

confidence: 99%

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Cryptography Based On Neural Network

Volná

Kotyrba

Kocian

et al. 2012

ECMS 2012 Proceedings Edited By: K. G. Troitzsch, M. Moehring, U. Lotzmann

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The goal of cryptography is to make it impossible to take a cipher and reproduce the original plain text without the corresponding key. With good cryptography, your messages are encrypted in such a way that brute force attacks against the algorithm or the key are all but impossible. Good cryptography gets its security by using incredibly long keys and using encryption algorithms that are resistant to other form attack. The neural net application represents a way of the next development in good cryptography. This paper deals with using neural network in cryptography, e.g. designing such neural network that would be practically used in the area of cryptography. This paper also includes an experimental demonstration.

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Section: Neural Cryptographymentioning

confidence: 99%

Section: Neural Cryptographymentioning

confidence: 99%

Cryptography Based On Neural Network

Volná

Kotyrba

Kocian

et al. 2012

ECMS 2012 Proceedings Edited By: K. G. Troitzsch, M. Moehring, U. Lotzmann

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“…Two types of neural networks have been studied in the context of the MG [17,18,19]. Beyond the mere academic question of how well or badly they can perform, it is worth noting that these papers were interested for the first time in interacting neural networks.…”

Section: Neural Networkmentioning

confidence: 99%

“…Refs [17,18] introduced simple perceptrons playing the minority game. Each perceptron i = 1, · · · , N is made up of M weights w i = (w 1 1 , · · · , w M 1 ) which are drawn at random before the game begins.…”

Section: Neural Networkmentioning

confidence: 99%

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Competition between Adaptive Agents: Learning and Collective Efficiency

Challet

2004

Collectives and the Design of Complex Systems

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We use the Minority Game and some of its variants to show how efficiency depends on learning in models of agents competing for limited resources. Exact results from statistical physics give a clear understanding of the phenomenology, and opens the way to the study of reverse problems. What agents can optimize and how well is discussed in details.1 Evolutionary models (see for instance [2,5,6,7]) are very different in nature, and are not reviewed here, mostly because they are not exactly solvable.

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