V. A. Erofeeva
St. Petersburg State University firstname.lastname@example.org
Key words: linear matrix inequalities, stochastic optimization, parameter tracking, nonstationary functional, multi-agent technologies, task distribution.
Due to significant advancements in embedded systems, sensor devices and wireless communication technology, sensor networks have attracted widespread attention in various areas of practical application. Such networks are difficult to control because of uncertainties among which are limited network throughput, measurement noise, asynchronous messaging, and energy efficiency. Under uncertain conditions, the stochastic approximation algorithms and linear matrix inequalities have proved to be very useful. In this paper we propose an approach for solving the task distribution optimization problem using linear matrix inequalities. Besides of that, we generalize the cyclic stochastic approximation algorithm and obtain the estimation properties. In addition, the cyclic stochastic approximation algorithm is applied to the distributed state estimation problem.
Bibliogr.: 49 refs.
N. A. Kizhaeva
St. Petersburg State University email@example.com
Key words: text mining, classification algorithms, clustering algorithmns.
A method for dynamic modeling of texts is introduced. Based on the proposed model two classification algorithms of text documents are designed. First classification rule is based on clustering of text documents using periodograms of times series constructed for each document. Second classification rule applies clustering with kernel based distances.
Bibliogr.: 32 refs.