It is proposed to use a neural network to calculate an approximation of the probabilistic-time characteristics of multichannel queuing systems (QS) with a "warm-up" and the unlimited capacity of the queue. From the results of numerical experiments, we observe a significant reduction in the complexity of computing probabilistic-time characteristics of the multi-channel QS with "warm-up" with minor errors of calculation of characteristics, compared with the numerical iterative algorithms. The advisability of the use of Bayesian regularization method for training a neural network and the best number of neurons are shown.
Abstract. We propose an approach to the automatic categorization of text documents based on the joint application of the method of latent semantic analysis (LSA) and fuzzy inference Mamdani algorithm. Method LSA is used for the semantic analysis of information in electronic document management systems by identifying semantic relationships between terms of documents and receipt of the compliance rate of the compared vectors. The rule base is proposed for fuzzy inference algorithm of Mamdani implementing the automatic rubrication of documents for a variety of given topics enabling automated monitoring of the distribution of documents not relevant to the specified topics, or having similarities in several thematic categories on the basis of the results of latent semantic analysis. Keywords: rubrication of documents; fuzzy inference; latent semantic analysis; the rule base; a fuzzy inference Mamdani algorithm.1. Введение. Целью работы является выработка подхода к решению задачи автоматической рубрикации документов по заданным тематическим рубрикам [1, 2]. Для этого предлагается использовать совместно метод латентно-семантического анализа и алгоритм нечёткого вывода Мамдани, что определяет новизну предлагаемого подхода.Для решения задачи автоматической рубрикации документов используются методы семантического анализа и автоматического разделения поступающей информации по заданным рубрикам.
Abstract. For cloud computing systems with web interface a set of probabilistic models is proposed. At the same time a model of Java applications with a web interface based on servlet and filters is considered. These models are based on queuing theory and extend its applications by studying the multichannel systems with "warm-up", "cooling" and phase-type approximation of Markovian and non-Markovian processes. Transition diagrams and matrices for the microstates of queuing systems being models of applications with a web interface are described, and a scheme for computing the stationary probability distributions for requests number, waiting time and expected time in system is being developed. The paper discusses the received computation results of the proposed modeling approach and their application to assessing the performance of the cloud systems using applications based on servlet and filters.
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