Informatica: An International Journal of Computing and Informatics

Control laws' design strategies are developed to stabilize a class of BIBO integro-differential systems with two distributed delays by using an extended system.

This paper deals with mathematical modelling of diabetes mellitus. A recent classification of diabetes mellitus is given and a new approach in constructing a mathematical model of this disease is described. The aim of mathematical modelling is to help a patient and his doctor in management of diabetes. The algorithms for solving inverse problems of coefficients reconstruction are investigated. Results of computational experiments are given.

In this paper we consider the problem of the distributed deadlock resolution. Starting from a high level specification of the problem and the resolution algorithm for a system with single request model, we provide successive levels of decreasing abstraction of the initial specification in order to achieve a solution in a complete distributed system. The successive refinements and the final distributed deadlock resolution algorithm are formaly described and proved by using the Input-Output Automata Model. The proposed solution is a modification of the algorithms in Mitchell and Merritt (1984) and Gonzalez de Mendívil et al. (1993) and preserves a similar message traffic to resolve a deadlock.

One objective of this paper is to estimate the parameters _p,d,q of an autoregressive fractionally integrated moving average ARFIMA (_p,d,q) stochastic model by minimizing the squares of the residuals using a Bayesian global optimization techniques. We consider bilinear model, too because it is the simple extension of linear model, defined by adding a bilinear term to traditional ARMA model. Therefore, the second objective of the paper is to estimate parameters of a bilinear time series.

The aim of the given paper is the development of optimal and tuned models and ordinary well-known on-line procedures of unknown parameter estimation for inverse systems (IS) using current observations to be processed. Such models of IS are worked out in the case of correlated additive noise acting on the output of the initial direct system (DS). The results of numerical investigation by means of computer (Table 1) are given.

The problem of construction of the fuzzy classification models (fuzzy classifiers) with high generalization ability is discussed. The strong self guessing property of fuzzy classificational models is introduced and examined. It is proved that this characteristic doesn't form a full system of restrictions, i.e., for the unambiguous detection of the most valid fuzzy classifier (among the set of fuzzy classifiers agreed with arbitrary learning set) it is necessary to use additional “regularizing” restrictions.

This work is our first attempt in establishing the connections between evolutionary computation algorithms and stochastic approximation procedures. By treating evolutionary algorithms as recursive stochastic procedures, we study both constant gain and decreasing step size algorithms. We formulate the problem in a rather general form, and supply the sufficient conditions for convergence (both with probability one, and in the weak sense). Among other things, our approach reveals the natural connection of the discrete iterations and the continuous dynamics (ordinary differential equations, and/or stochastic differential equations). We hope that this attempt will open up a new horizon for further research and lead to in depth understanding of the underlying algorithms.