@misc{Huk_Maciej_Wybrane_2005,
 author={Huk, Maciej},
 year={2005},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 description={Prace Naukowe Akademii Ekonomicznej we Wrocławiu; 2005; nr 1064, s. 80-87},
 publisher={Wydawnictwo Akademii Ekonomicznej im. Oskara Langego we Wrocławiu},
 language={pol},
 abstract={This article presents the results of research on the Sigma-if neural network model. In spite of its simple structure and the use of standard, synchronous working and learning methods, it possesses important properties that are unattainable for classic perceptron networks. Due to expanding the domain of neuronal activation functions to include the time dimension and by extending the intemeuronal connection attribute set, this structure realises the idea of nondestructive intemeuronal connection elimination. It is capable - when working as well as when training - of excluding intemeuronal connections which are irrelevant at a given moment, without completely eliminating the possibility of using them in other cases. Special attention is devoted to the possibility of correct solving the linearly inseparable problems by single Sigma-if neuron, even though it uses simple sigmoidal threshold function (as in case of classical perceptron, which hasn’t such properties) Analysis of this fact is supplemented by its biological interpretation. Results of experiments are presented along with graphical illustrations of decision spaces of example functioning models. The presentation mentioned above provides the necessary background for a discussion on the networks’ mathematical functional model and properties. The author derives functions, which describe the dynamics of each individual Sigma if neuron and the automaton as a whole. The theoretical description is supplemented by a number of application examples, which show the legitimacy and promise of the Sigma-if model. The article is augmented by descriptions of key experiments, along with analyses of their results.},
 title={Wybrane właściwości sieci neuronowej Sigma-if},
 type={artykuł},
}