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Identification methods based on associative search procedure

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Bakhtadze N. N. , Kulba V. V. , Yadikin I. B. , Lototsky V. A. , Maximov E. M.

Management and Production Engineering Review

2011

tom Vol. 2, No. 3

6-18

In modern control systems, identification is an integral part of adaptive control where process models are adjusted using real-time operation data and control actions optimal with respect to some performance criterion are developed. A variety of identification methods based on mathematical statistics techniques have been developed. Algorithms optimal for certain classes of objects and external disturbances were categorized dependent on the available a priori information about the control object. The limits of approximating models development and application were outlined. Against this background, the paper presents novel associative search techniques enabling the development of a new dynamic object's model on each time step rather than plant approximation pertaining to time. The model is build using the data samples from process history (associations) developed at the learning phase. The new techniques employs the models of human individual's (process operator's, stock analyst's or trader's) behavior based on professional knowledge formalization. Application examples from oil refining and chemical industries, power engineering, and banking are adduced.

An approach to coordination of power generating units

100%

Bakhtadze N. N. , Yadikin I. B. , Kulba V. V. , Lototsky V. A. , Maximov E. M.

Control and Cybernetics

2010

tom Vol. 39, no 1

305-318

The importance of the problem of power generating facility identification is justified against the background of interconnection of the European and Russian power grids. Intelligent control techniques for power generation facilities are presented. A methodology for estimating the dynamics of participation of the power grid generating facilities in the overall primary frequency regulation in contingency situations is developed based on frequency and generating capacity time series. Process identification algorithms, based on virtual model design using process data archives and knowledge bases, are discussed. Associative search methods are used for identification algorithm development.