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Moduł translacji języka MATLAB na C#, wspomagający tworzenie programów symulacji ciągłych układów dynamicznych, działających w środowisku uruchomieniowym .NET

Augustyn D., Kunc S.

Studia Informatica

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2010

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Vol. 31, nr 3

77-94

PL

Artykuł omawia budowę i zastosowanie zaproponowanego translatora języka MATLAB na język C#, w zakresie kodu funkcji opisujących równania stanu ciągłego układu dynamicznego. Translator M2NET, na podstawie kodu M funkcji, tworzy opis dynamiki w postaci komponentów – pliku źródłowego w języku C# albo biblioteki DLL. Użycie translatora powala na wykorzystanie licznych dostępnych zasobów w postaci skryptów z opisami dynamiki, wcześniej utworzonych i przetestowanych w popularnym środowisku obliczeniowym MATLAB. Rezultaty działania translatora mogą być bezpośrednio wykorzystane przy budowie programów symulacyjnych, działających w środowisku uruchomieniowym .NET Framework. W szczególności mogą być wykorzystane przy tworzeniu efektywnych, zrównoleglonych programów symulacyjnych, zbudowanych na podstawie modułu Parallel Extensions to .NET Framework.

EN

The paper describes an architecture and application of a proposed translator from MATLAB to C#. It translates source code of functions implementing state equations of continuous dynamical systems. Using a code of a M-function the translator named M2NET, creates a description of a dynamical system as a C# source code file or a managed library. The translator lets use numerous resources – functions described different dynamical systems, previously created and tested in MATLAB, the popular computing system. Results of the translation can be used directly for creation of .NET-based simulation programs. Particularly, they can be used for developing effective parallelized simulation programs based on Parallel Extensions to .NET Framework module.

2

Real–valued GCS classifier system

Cielecki Ł., Unold O.

International Journal of Applied Mathematics and Computer Science

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2007

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Vol. 17, no 4

539-547

EN

Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.

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Multigenerative Grammar Systems

Meduna A., Lukas R.

Schedae Informaticae

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2006

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Vol. 15

175-188

EN

This paper presents new models for all recursive enumerable languages. These models are based on multigenerative grammar systems that simultaneously generate several strings in a parallel way. The components of these models are context -free grammars, working in a leftmost way. The rewritten nonterminals are determined by a finite set of nonterminal sequences.

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Analiza wpływu wybranych parametrów na efektywność systemu GCS

Unold O.

Informatyka Teoretyczna i Stosowana

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2005

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R. 5, nr 8

177-189

PL

Przedstawiono nowy model uczącego się systemu klasyfikującego, w którym klasyfikatory reprezentowane są przez produkcję gramatyki bezkontekstowej podane w postaci normaluej Chomsky'ego. System GCS (ang. grammar-based classifier system) odkrywa nowe reguły gramatyki stosując metodę tzw. pokrywania (ang. covering) oraz algorytm genetyczny. Efektywność systemu zależy w znacznej mierze od odpowiedniego ustawienia jego parametrów. W pracy przebadano zależność efektywności od liczności populacji, płodności oraz symbolu dont care.

EN

Grammar-based classifier system (GCS) is a new version of Learning Classifier Systems (LCS) in which classifi-ers arf represented by context-free grammar in Chomsky Normal Form. GCS works basically like all other LCS models but it differs from łbem (i) in the covering, (ii) in the matching, and (iii) in representation. Performance of GCS depends on many parameters. In this paper the influance of size of population, fertility and don't care symbol was tested. The set of experiments was performed on context-free grammar called toy-grammar. The experiments revealed some interesting properties of this faetors and showed that this parameters must be tuned very carefully and knowingly in order to improve performance of the GCS.

5

Context-free grammar induction with grammar-based classifier system

Unold O.

Archives of Control Sciences

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2005

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Vol. 15, no. 4

681-690

EN

In the paper we deal with the induction of context-free grammars from sample sentences. We present some extensions to grammar-based classifier system (GCS) that evolves population of classifiers represented by context-free grammar productions rules in Chomsky Normal Form. GCS is a new version of Learning Classifier Systems but it differs from it in the covering, in the matching, and in representation. We modify the discovering component of the GCS and apply system for inferring such context-free languages as toy language, and grammar for large corpora of part-of-speech tagged natural English language. For each task a set of experiments was performed.

6

On a construction of context-free gramma

Martinek P.

Fundamenta Informaticae

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2000

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Vol. 44, Nr 3

245-264

EN

The grammatical inference problem is solved for the class of context-free languages. A context-free language is supposed to be given by means of all its strings. Considering all strings of length bounded by k, context-free grammars G(j,k) with 1 < j < k are constructed. A continual increasing of the index k leads to an infinite sequence. It is proved that G(j,k) are equivalent for all j > j(o), k > k(o) with some positive integers j(o), k(o). Moreover, the equivalences among these grammars can be recognized on the basis of involved nonterminals and productions only.

7

On entropy and optimal coding cost for stochastic language

Zhiltsova L.

Fundamenta Informaticae

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1998

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Vol. 36, nr 2,3

285-305

EN

We investigate questions, relating to optimal binary coding of a language, for which a probability distribution is given on its words (for a stochastic language). As optimal we understand the coding, which gives minimum of mathematical expectation of a length of the coded word, or minimum of the coding cost. For any stochastic language with a finite value of the entropy we establish lower and upper bounds of the optimal coding cost, dependent only on the entropy, and we prove their unimprovability. For any stochastic context-free language with unique derivation it is found necessary and sufficient condition of existence of finite values of the optimal coding cost and the entropy. Also an effective method of calculation of the entropy is found for the case when the considered condition holds.