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EN
Flow graphs are the effective tool for description of the complicated microwave measurement systems and usually are used in the process of creating measurement equation. The algorithm which can find this equation is known as the Mason's gain formula (MGF). This operation needs to perform a lot of symbolic computations so performing of this algorithm without proper software is heavy and is vulnerable to appearing some errors. This paper shows which existing scripts for MATLAB can be use to perform this task.
PL
Grafy przepływu są skutecznym narzędziem umożliwiającym opis skomplikowanych układów pomiarowych w technice mikrofalowej i zwykle są stosowane w procesie tworzenia równania pomiaru. Algorytm umożliwiający znalezienie takiego równania jest znany jako reguła wzmocnienia Masona. Wymaga on znacznej liczby obliczeń symbolicznych toteż jego realizacja bez zastosowania oprogramowania jest uciążliwa oraz podatna na wystąpienie błędów podczas wykonywania przekształceń. W niniejszym artykule wskazano możliwość zastosowania istniejących już skryptów programu MATLAB do realizacji tego zadania.
EN
In the paper, we focus on ant-based clustering time series data represented by means of the so-called delta episode information systems. A clustering process is made on the basis of delta representation of time series, i.e., we are interested in characters of changes between two consecutive data points in time series instead of original data points. Most algorithms use similarity measures to compare time series. In the paper, we propose to use a measure based on temporal rough set flow graphs. This measure has a probabilistic character and it is considered in terms of the Decision Theoretic Rough Set (DTRS) model. To perform ant-based clustering, the algorithm based on the versions proposed by J. Deneubourg, E. Lumer and B. Faieta as well as J. Handl et al. is used.
EN
A new method of first-, second-order and multiparameter symbolic sensitivity determination based on the nullor model of active devices and modified Coates flow graph is presented. Rules for a symbolic reduction of nullor circuit complexity are described. An algorithm performs symbolic sensitivity analysis with respect to various circuit parameters appeared not only at one location in the modified Coates flow graph. Advantages of the method suggested are that, the matrix inversion is not required and the main drawback of some methods based on the adjoint graph, i.e. the necessity to analyze the corresponding graph twice, is avoided. Illustrative examples on symbolic sensitivity analysis are given.
4
Content available remote Flow Graphs and Intelligent Data Analysis
EN
This paper concerns a new approach to intelligent data analysis based on information flow distribution study in a flow graph. Branches of a flow graph are interpreted as decision rules, whereas a flow graph is supposed to describe a decision algorithm. We propose to model decision processes as flow graphs and analyze decisions in terms of flow spreading in a graph.
PL
Przedstawiono zagadnienie modelowania i analizy drgających układów ciągłych za pomocą grafów przepływowych. Wykorzystanie metody grafów przepływowych pokazano na przykładzie układów drgających wzdłużnie.
EN
The paper presents a method of design and analysis of continuous vibrating systems by means of flow graphs. On examples of longitudinal vibrating continuous models an application of flow graphs has been shown.
EN
This paper concerns some relationship between Bayes' theorem and rough sets. It is revealed that any decision algorithm satisfies Bayes' theorem, without referring to either prior or posterior probabilities inherently associated with classical Bayesian methodology. This leads to a new simple form of this theorem, which results in new algorithms and applications. Besides, it is shown that with every decision algorithm a flow graph can be associated. Bayes' theorem can be viewed as a flow conservation rule of information flow in the graph. Moreover, to every flow graph the Euclidean space can be assigned. Points of the space represent decisions specified by the decision algorithm, and distance between points depicts distance between decisions in the decision algorithm.
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