Autori: Theodor D. Popescu

Editorial: V. Mladenov, K. Psarris, N. Mastorakis, A. Caballero, G. Vachtsevanos, Mathematical Models for Engineering Science, 1, p.63-67, 2010.

Rezumat:

The problem of change detection and data segmentation has received
considerable attention in a research context and appears to be the
central issue in various application areas. The change detection and
segmentation model used in this paper is the simplest extension of
the linear regression models to data with abruptly changing
properties. In the first part of the paper we give a general view on
the main techniques used in change detection and segmentation:
filtering techniques with a whiteness test and techniques based on
sliding windows and distance measures. A new algorithm based on a
likelihood technique, when sliding windows are used, for diagnosis
of model parameter and variance changes is then presented. The results of some Monte-Carlo simulation for detection and diagnosis of model parameter and variance changes are included in the paper.

Cuvinte cheie: Change detection, Diagnosis, Regression models, Decision making, Distance measure, Likelihood techniques