Probabilistic advisory systems for data-intensive applications

International Journal of Adaptive Control and Signal Processing

Volumn 17, Issue 2, 2003, Pages 133-148

  Quinn, A ^a   Ettler, P ^b   Jirsa, L ^c   Nagy, I ^d   Nedoma, P ^c  

^a TRINITY COLLEGE DUBLIN   (Ireland)

^b COMPUREG Plzeň sro ^*  (Czech Republic)

^c Pod Vodárenskou v í 4   (Czech Republic)

^d CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

Advisory system; Application in transport; Bayesian inference; Control design; Manufacturing; Medicine; Mixture of dynamic models

Indexed keywords

CONTROL SYSTEM SYNTHESIS; ESTIMATION; INFERENCE ENGINES; MATHEMATICAL MODELS; PROBABILITY; RECURSIVE FUNCTIONS;

BAYESIAN INFERENCE; DYNAMIC MODELS; PROBABILISTIC ADVISORY SYSTEM;

ADAPTIVE CONTROL SYSTEMS;

EID: 0037744554     PISSN: 08906327     EISSN: None     Source Type: Journal    
DOI: 10.1002/acs.743     Document Type: Article

References (13)

1. Aguilarmartin J. Qualitative control, diagnostic and supervision of complex processes. Mathematics and Computers In Simulation 1994; 36(2):115-127.
2. Polymenis A, Titterington DM. On the determination of the number of components in a mixture. Statistics and Probability Letters 1998; 4:295-298.
3. Kárný M, Bohm J, Guy TV, Nedoma P. Mixture-based adaptive probabilistic control. International Journal of Adaptive Control and Signal Processing 2003; 17:119-132.
4. Rosenkrantz RD (ed.). E.T. Jaynes: Papers on Probability, Statistics and Statistical Physics. D. Reidel: Dordrecht-Holland, 1983
5. Helbing D. Traffic data and their implications for consistent traffic flow modelling. Transportation Systems, Chania, Greece, 1997, 809-814.
6. Project IST-1999-12058. Decision Support Tool for Complex Industrial Processes Based on Probabilistic Data Clustering (ProDaCTool), 1999-2002.
7. Harbert J, Eckelman W, Neumann R. Nuclear Medicine. Diagnosis and Therapy. Thieme Medical Publishers Inc: New York, 1996.
8. Jirsa L, Quinn A. Mixture analysis of nuclear medicine data: medical decision support. Proceedings of the Irish Signals and Systems Conference, NUI Maynooth, 2001; 393-398.
9. Nedoma P. Kárný M, Nagy I, MixTools, MATLAB toolbox for mixtures: user's guide. Technical Report, ÚTIA AV ČR, Praha, 2001 (Deliverable of the ProDaCTool project).
10. Ettler P, Jirkovský F. Digital controllers for SKODA rolling mills. In Advanced Methods in Adaptive Control for Industrial Applications, Lecture Notes in Control and Information Sciences, No. 158. Springer: Berlin/Heidelberg, 1991; 31-35.
11. Kárný M, Böhm J, Guy T, Jirsa L, Nagy I, Nedoma P, Quinn A, Tesař L, Maisey M, Tichý M. ProDaCTool background. Technical Report, ÚTIA AV ČR, Praha, 2001 (Deliverable of the ProDaCTool project).
12. Tesař L, Quinn A. Method for artefact detection and suppression using α-stable distributions. Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA), Prague, 2001.
13. Siegel GA, Thomas SR, Stubbs JB, Stabin MG, Hays MT. MIRD pamphlet No.16: Techniques for quantitative radiopharmaceutical bio-distribution, data acquisition and analysis. Journal of Nuclear Medicine 1999; 40(2):37-61.