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Neural networks can synchronize by learning from each other. For that purpose they receive common inputs and exchange their outputs. Adjusting discrete weights according to a suitable learning rule then leads to full synchronization in a finite number of steps. It is also possible to train additional neural networks by using the inputs and outputs generated during this process as examples. Several algorithms for both tasks are presented and analyzed. In the case of Tree Parity Machines the dynamics of both processes is driven by attractive and repulsive stochastic forces. Thus it can be described well by models based on random walks, which represent either the weights themselves or order parameters of their distribution. However, synchronization is much faster than learning. This effect is caused by different frequencies of attractive and repulsive steps, as only neural networks interacting with each other are able to skip unsuitable inputs. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. If the synaptic depth is increased, the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Therefore the partners can reach any desired level of security by choosing suitable parameters. In addition, the entropy of the weight distribution is used to determine the effective number of keys, which are generated in different runs of the key-exchange protocol using the same sequence of input vectors. If the common random inputs are replaced with queries, synchronization is possible, too. However, the partners have more control over the difficulty of the key exchange and the attacks. Therefore they can improve the security without increasing the average synchronization time.
Die vorliegende Arbeit hat zum Ziel, das Antwortverhalten nichtlinearer Reaktionen auf zielgerichtete Störungen zu untersuchen. Dabei beschäftigt sie sich mit zwei nichtlinearen chemischen Sauerstoff-Oszillatoren. Bei den beiden nichtlinearen chemischen Reaktionen handelt es sich um den Polyacrylamid-Methylenblau-Sauerstoff- (PA-MBO) Oszillator und um die Kupfer(II)ionen katalysierte Oxidation von Ascorbinsäure durch Luftsauerstoff. Im ersten Fall wird durch selektive Belichtung des Reaktionsmediums die gebildete Geloberfläche durch ein computergenerirtes Muster kodiert. Die Systemantwort wird mit Hilfe einer CCD-Kamera aufgenommen und danach einer Analyse unterzogen. Die erhaltenen Ergebnisse werden anschließend durch eine Computersimulation verifiziert. Die zweite untersuchte Möglichkeit, das PA-MBO-System einer Störung zu unterwerfen, ist das Anlegen eines externen elektrischen Feldes. In einer speziell dafür entworfenen Anordnung bildet sich ein quasi-eindimensionales Turing-Muster. In dieser quasi-eindimensionalen Anordnung kann die Reaktion leicht elektrischen Strömen von bis zu 200 mA/cm2 ausgesetzt werden. Die experimentellen Daten werden anschließend der Karhunen-Loeve Zerlegung unterworfen, um die komplexe Dynamik der Systemantwort zu studieren. Die Oxidation von Ascorbinsäure durch Luftsauerstoff in Gegenwart von Kupfer(II)ionen, wird im CSTR durchgeführt. Dabei läßt sich das Phänomen der stochastischen Resonanz beobachten, wenn man die Flußrate sinusförmig moduliert und dieser Frequenz zusätzlich weißes Rauschen überlagert.