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A hybrid approach to dimension reduction in classification

Krawczak M., Szkatuła G.

Control and Cybernetics

2011

Vol. 40, no 2

527-551

In this paper we introduce a hybrid approach to data series classification. The approach is based on the concept of aggregated upper and lower envelopes, and the principal components here called 'essential attributes', generated by multilayer neural networks. The essential attributes are represented by outputs of hidden layer neurons. Next, the real valued essential attributes are nominalized and symbolic data series representation is obtained. The symbolic representation is used to generate decision rules in the IF. . . THEN. . . form for data series classification. The approach reduces the dimension of data series. The efficiency of the approach was verified by considering numerical examples.

Military applications of neural networks

Świątnicki Z.

Biuletyn Wojskowej Akademii Technicznej

1999

Vol. 48, nr 11

183-190

The main applications of neural networks in military systems were described. The idea of neurocomputer was shown. Application of neural networks in military robots was presented. The main tasks of neural networks in military systems were discussed.

W pracy omówiono podstawowe wojskowe zastosowania sieci neuronowych. Opisano ideę neurokomputera. Zostało zaprezentowane wykorzystanie sieci neuronowych do konstrułowania robotów wojskowych. Przedstawiono próbę klasyfikacji zadań realizowanych przez sieci neuronowe.