Hidden attractors in fundamental problems and engineering models

Recently a concept of self-excited and hidden attractors was suggested: an attractor is called a self-excited attractor if its basin of attraction overlaps with neighborhood of an equilibrium, otherwise it is called a hidden attractor. For example, hidden attractors are attractors in systems with no equilibria or with only one stable equilibrium (a special case of multistability and coexistence of attractors). While coexisting self-excited attractors can be found using the standard computational procedure, there is no standard way of predicting the existence or coexistence of hidden attractors in a system. In this plenary survey lecture the concept of self-excited and hidden attractors is discussed, and various corresponding examples of self-excited and hidden attractors are considered

Fast Independent Component Analysis in Kernel Feature Spaces

Abstract. It is common practice to apply linear or nonlinear feature extraction methods before classification. Usually linear methods are faster and simpler than nonlinear ones but an idea successfully employed in the nonlinearization of Support Vector Machines permits a simple and effective extension of several statistical methods to their nonlinear counterparts. In this paper we follow this general nonlinearization approach in the context of Independent Component Analysis, which is a general purpose statistical method for blind source separation and feature extraction. In addition, nonlinearized formulae are furnished along with an illustration of the usefulness of the proposed method as an unsupervised feature extractor for the classification of Hungarian phonemes

Choosing the two finalists

Choice correspondence, Two-stage choice, Consideration sets, Axiomatization, D01,