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Simulating medical objects simulation using an artififfcial neural network whose structure is based on adaptive resonance theory (2011)

Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Zenkova, Natalia ; Troitzsch, Klaus G. ; Sletkov, Denis V.

This paper describes artififfcial neural networks which are based on the adaptive resonance theory. The usage of these artiffficial neural networks for classification tasks is presented. The example uses is the classifffication of patient health from the results of general blood analysis.

The prediction of currency exchange rates using artificial neural networks (2011)

Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Troitzsch, Klaus G.

This paper describes the analysis of a neural network used to predict currency exchange rates in comparison to technical analysis. The neural network structures used are a multilayer perceptron and a Volterra network.

A universal simulator based on artificial neural networks for computer clusters (2011)

Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Troitzsch, Klaus G.

This paper describes parallel algorithms for training artifcial neural networks. Possible levels of parallelity are presented. Experiments for checking the effciency of algorithms are discussed.

A parallel algorithm for selecting activation functions of an artificial network (2011)

Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Troitzsch, Klaus G.

This paper describes a parallel algorithm for selecting activation functionsrnof an artifcial network. For checking the efficiency of this algorithm a count of multiplicative and additive operations is used.

Comparing the efficiency of serial and parallel algorithms for training artificial neural networks using computer clusters (2011)

Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Troitzsch, Klaus G.

An estimation of the number of multiplication and addition operations for training artififfcial neural networks by means of consecutive and parallel algorithms on a computer cluster is carried out. The evaluation of the efficiency of these algorithms is developed. The multilayer perceptron, the Volterra network and the cascade-correlation network are used as structures of artififfcial neural networks. Different methods of non-linear programming such as gradient and non-gradient methods are used for the calculation of the weight coefficients.

Simulating social objects with an artiffficial neural network using a computer cluster (2011)

Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Troitzsch, Klaus G. ; Zenkova, Natalia

This paper describes results of the simulation of social objects, the dependence of schoolchildren's professional abilities on their personal characteristics. The simulation tool is the artififfcial neural network (ANN) technology. Results of a comparison of the time expense for training the ANN and for calculating the weight coefficients with serial and parallel algorithms, respectively, are presented.

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