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On classification with missing data using rough-neuro-fuzzy systems

100%

Nowicki R.

International Journal of Applied Mathematics and Computer Science

2010

tom 20

nr 1

55-67

The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.

A finite-buffer queue with a single vacation policy: an analytical study with evolutionary positioning

51%

Woźniak M., Kempa W., Gabryel M., Nowicki R.

International Journal of Applied Mathematics and Computer Science

2014

tom 24

nr 4

887-900

In this paper, application of an evolutionary strategy to positioning a GI/M/1/N-type finite-buffer queueing system with exhaustive service and a single vacation policy is presented. The examined object is modeled by a conditional joint transform of the first busy period, the first idle time and the number of packets completely served during the first busy period. A mathematical model is defined recursively by means of input distributions. In the paper, an analytical study and numerical experiments are presented. A cost optimization problem is solved using an evolutionary strategy for a class of queueing systems described by exponential and Erlang distributions.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Nowicki R.

1 Gabryel M.

1 Kempa W.

1 Woźniak M.

1 2014

1 2010

Wyniki wyszukiwania

On classification with missing data using rough-neuro-fuzzy systems

A finite-buffer queue with a single vacation policy: an analytical study with evolutionary positioning