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This note considers the global stabilization problem for a class of nonlinear systems with unknown power (exponent) drifts. Based on the concept of interval homogeneity with monotone degrees, the allowable bounds of the unknown power drifts can be explicitly determined to guarantee the solvability of the problem. The technique of adding a power integrator is revamped based on a new Lyapunov function with interval parameters and is recursively employed to construct a global stabilizer for the nonlinear systems.Index Terms-Global stabilization, p-normal form, unknown power drift, interval homogeneity, adding a power integrator

This paper introduces a new evidential clustering method based on the notion of "belief peaks" in the framework of belief functions. The basic idea is that all data objects in the neighborhood of each sample provide pieces of evidence that induce belief on the possibility of such sample to become a cluster center. A sample having higher belief than its neighbors and located far away from other local maxima is then characterized as cluster center. Finally, a credal partition is created by minimizing an objective function with the fixed cluster centers. An adaptive distance metric is used to fit for unknown shapes of data structures. We show that the proposed evidential clustering procedure has very good performance with an ability to reveal the data structure in the form of a credal partition, from which hard, fuzzy, possibilistic and rough partitions can be derived. Simulations on synthetic and real-world datasets validate our conclusions.

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