Edmond Nicolau, A.T. Murgan, M.A. Pascadi and M.V. Pascadi
This communication highlights the study of the properties of a general second‐order nonlinear multistable system. Particular attention is directed to fixed points analysis and…
Abstract
This communication highlights the study of the properties of a general second‐order nonlinear multistable system. Particular attention is directed to fixed points analysis and phase portrait representation. Some examples and computer simulations are also included.
Manuela A. Pascadi and Mihai V. Pascadi
Describes a new kind of non‐linear trainable classifier, successfully tested in computer‐vision pattern recognition. Class regions are not described, as usually, through…
Abstract
Describes a new kind of non‐linear trainable classifier, successfully tested in computer‐vision pattern recognition. Class regions are not described, as usually, through analytical means but as a reunion of standard sets. Defines the notion of E‐separability for the class regions in the feature space IRd considered as a metric space with a distance related to the Euclidean distance. Studies and proves the convergence of the decision regions to the class regions in this metric space. For a given E (is a member of) provides a stopping rule for the training phase. Then describes the working phase, showing how classification actually takes place. Finally, presents significant results.