The problem of the evaluation of fuzzy situations arises quite often in real life. Images, symbols, signals and so on usually are fuzzily described and can be considered as fuzzy situations. So the parameters, which characterize the fuzzy situation, are not only numerical ones, but verbal too, for example, an object can be described as "heavy, little and moves fast". It is difficult to decide something formally about the object with such characterization. Leasing companies meet this problem in contracts classification process too. Accordingly most leasing companies have own contracts classification (recognition) systems, which mostly are statistically based and the similarities and differences between good and bad contracts are not computed. Therefore the demand arises to develop the intellectual recognition system using fuzzy logic. This system should be simply integrating in any leasing company. It also must compute and measure the similarities and differences between good and bad contracts from their evaluation history. Such intellectual system could be as an adviser for business expert. The module of fuzzily characterized situation's evaluation is implemented using: 1) fuzzy sets, 2) fuzzy clustering, 3) fuzzy recognition. The general idea was experimentally investigated using artificially generated data as well as using data from real contracts. The software developed during this research is under preparation to be integrated in companies "Baltic... [to full text]

Prognozuojant finansinių fondų dinamiką, dažniausiai sunku pasirinkti vieną iš daugelio esamų prognozavimo metodų, nes kiekvienas jų turi pranašumų ir trūkumų. Atlikta plačiai naudojamų prognozavimo metodų Soros, Bayeso, AR, Wiener analizė. Plačiai prognozavimui naudojami matematinės statistikos metodai, tačiau dažnai jie nagrinėja funkcijas su dideliais kintamųjų skaičiais. Tai daugelio diskretinių optimizavimo problemų atvejai. Norint apriboti klaidų skaičių reikia daugiau skaičiavimo bandymų. Dažnai bandymų skaičius turi didėti eksponentiškai. Gali būti kad įvykių statistikoje nėra duomenų kaip elgtis pagal tam tikrą situaciją. Dėl šių ir kitų priežasčių ieškoti kiti prognozavimo metodai. Šiame darbe finansinių fondų dinamikai prognozuoti naudojama miglotoji logika. Sukurta sistema prognozuoja įvykių grupių poveikius fondų kreivei, o ne remiasi statistika. Tai ekspertinis prognozavimo metodas. Miglotoji logika suteikia galimybę ekspertams analizuoti ir nusakyti probleminę sritį miglotomis taisyklėmis. Miglotosios taisyklės sudaromos iš žmogui suprantamų teiginių. Jomis ekspertai gali nustatyti sistemos įėjimo duomenis. Tokių įėjimo duomenų paklaida priklauso nuo specialistų patirties ir intuicijos įvertinti įvykių grupių poveikį fondų kreivei. Šiame darbe analizuoti fondų kreivės aproksimavimo būdai naudojant interpoliavimo ir suglodinimo metodus. Taip pat analizuotas Sklansky ir Gonzalez aproksimavimo algoritmas, kuris buvo realizuotas jį pamodifikavus. Bei pateikti... [toliau žr. visą tekstą] / Forecasting the dynamics of financial funds, often are difficult to choose one of the many existing prediction methods, because each of them has advantages and disadvantages. Performed widely used forecasting techniques Soros, Bayeso, AR, Wiener analysis. It is widely used in prediction methods of mathematical statistics. Often mathematical statistics methods examine the functions of the large number of variables. Large numbers of variables are the main problems in many cases for discreet optimization methods. To limit the number of errors in calculating it is need for more testing. Often number of experiments should increase exponentially. It is possible that the statistics of events do not behave according to the data of a certain situation. For these and other reasons there was seek for other forecasting methods. In this work, fuzzy logic used to predict the dynamics of financial funds. The developed system predicts event’s groups effects of funds curve, and are not based on statistics. This is expert prediction method. Fuzzy logic makes possible for experts to analyze and define the problematic area of vague rules. Fuzzy rules are made from statements which are easy to understand. Experts can detect the input data. Such input data error depends on the expert experience and intuition to assess the impact of events groups funds curve. In this work where analyzed approximation methods as interpolation and smoothing the curve. Approximation algorithm of Sklansky and Gonzalez... [to full text]

Presently information systems are increasingly penetrating to our daily life. Recently it is relevant to integrate the newest technologies. In that way traditional system becomes “smart” who are more economical, optimal, and self-sufficient. The biggest problem is to make a model of “smart” system. There were analyzed modeling methods, heating and cooling control systems in this job. Mathematical model for heating and cooling controller using fuzzy logic was presented. According to analyzed problems it was made verification with Matlab during experimental phase. There was made comparison evaluation of mathematical model made with fuzzy logic and timed Petri nets.