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The design and implementation of the system supporting the control of industrial-like processes

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Języki publikacji
EN
Abstrakty
EN
The software design and the implementation of an intelligent system supporting the control of industrial-like processes is discussed in the paper. The main tasks of such a system are on-line monitoring, analysis and recognition of process patterns (the results of the recognition are to be used to control the process), and self-learning activity, which enable the system automatic gathering new information about the process in order to recognise new process patterns. The system is based on the syntactic pattern recognition paradigm with the use of quasi-context sensitive string grammars. The design model of the system has been prepared accordingly to the object-oriented approach. In the paper we present the overview of the model and its implementation, and we discuss its advantages.
Rocznik
Tom
Strony
51--66
Opis fizyczny
Bibliogr. 11 poz.
Twórcy
autor
Bibliografia
  • [1]Arlow J., Neustadt I., UML 2 and The Unified Process. Practical Object-Oriented Analysis and Design, 2nd ed., Addison-Wesley, 2005.
  • [2]Booch G., Rumbaugh J., Jacobson I., The Unified Modeling Language User Guide, 2nd edition, Addison-Wesley, 2005.
  • [3]Flasinski M., Jurek J., Dynamically Programmed Automata for Quasi Context Sensitive Languages as a Tool for Inference Support in Pattern Recognition-Based Real-Time Control Expert Systems, Pattern Recognition, 1999, 32, 671-690.
  • [4]Fu K.S., Syntactic Pattern Recognition and Applications, Prentice Hall, 1982.
  • [5] Jurek J., On the Linear Computational Complexity of the Parser for Quasi Context Sensitive Languages, Pattern Recognition Letters, 2000, 21, 179-187.
  • [6]Jurek J., Towards grammatical inferencing of GDPLL(k) grammars for applications in syntactic pattern recognition-based expert systems, Lecture Notes in Computer Science, 2004, 3070, 604-609.
  • [7]Jurek J., Recent developments of the syntactic pattern recognition model based on quasi-context sensitive languages, Pattern Recognition Letters, 2005, 26, 1011-1018.
  • [8]Jurek J., Generalisation of a Language Sample for Grammatical Inference of GDPLL(k) Grammars, Computer Recognition Systems 2 - Advances in Soft Computing series, Berlin-Heidelberg, Springer Verlag, 2007, 282-288.
  • [9]Jurek J., Peszek T., On the use of syntactic pattern recognition methods, neural networks, and fuzzy systems for short-term electrical load forecasting, Computer Recognition Systems - Advances in Soft Computing series, Berlin-Heidelberg-New York, Springer Verlag, 2005, 851-858.
  • [10]Negnevitsky M., Artificial Intelligence. A Guide to Intelligent Systems, Addison-- Wesley, 2002.
  • [11]Rumbaugh J., Jacobson I., G. Booch, The Unified Modeling Language Reference Manual, Addison-Wesley, 2004.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0089-0077
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