Traditional methods of dealing with variability in simulation input data are mainly stochastic. This is most often the best method to use if the factors affecting the variation or...
In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a ne...
Neural networks and the Kriging method are compared for constructing £tness approximation models in evolutionary optimization algorithms. The two models are applied in an identica...
We introduce CUI networks, a compact graphical representation of utility functions over multiple attributes. CUI networks model multiattribute utility functions using the well stu...
Networks estimating probability density are usually based on radial basis function of the same type. Feature Space Mapping constructive network based on separable functions, optimi...
Wlodzislaw Duch, Rafal Adamczak, Geerd H. F. Dierc...