Abstract
The response characteristic between input and output variables can be modelled by knowledge-based methods of signal processing like Fuzzy Logic. Based on a low number of data sets Fuzzy Logic can be applied advantageously for non-linear processes, especially. The rule-based description of the non-linear process behaviour can be realised by means of different structures of the fuzzy algorithm. The paper presents and compares three structure variants (complex, parallel and cascaded structure) of the fuzzy model design to reproduce the input-output behaviour. The structure analysis was carried out for the fuzzy-based modelling of parameters which are necessary to describe the process state of pressure vessels with water-steam mixture during accidental depressurizations.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kästner, W., Fenske, A., Hampel, R.: Improvement of the Robustness of Model-Based Measuring Methods Using Fuzzy Logic. In: World Scientific, Proceedings of the 3“ International FUNS Workshop. Antwerp, Belgium (1998) 129–142
Chaker, N., Wagenknecht, M., Hampel, R.: Fuzzy Controller Structure Transformation. World Scientific, Proceedings of the 3’1 International FLINS Workshop. Antwerp, Belgium (1998)99–110
Frank, P.M.: Fuzzy supervision — Application of Fuzzy Logic to Process Supervision and Fault Diagnosis. In: Proc. Int. Workshop on Fuzzy Intelligent Systems, Duisburg, Germany (1994) 36–59
Patton, R.J.: Fuzzy Observers for Non-linear Dynamic Systems Fault Diagnosis. In: Proc. of the 37th IEEE Conf. On Decision & Control, Tampa, Florida, USA (1998) 84–89
Piecz•ski, A.: An Expert System with Fuzzy Knowledge Base. In: Proc. 3“. Int. Conf. New Trends in Automation of Energetic Processes’98, Zlin, Czech Republic (1998) 374–378
Piecz•ski, A.: Integrated Neural Network and Fuzzy Knowledge Base System for Fault Detection and Isolation in Steam Power Plant. In: Proc. of the Fifth Int. Symp. on Methods and Models in Automation and Robotics, MMAR’98, Miedzyzdroje, Poland (1998) 707–712
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pieczynski, A., Kästner, W. (2000). Fuzzy Modelling of Multidimensional Non-linear Processes — Design and Analysis of Structures. In: Hampel, R., Wagenknecht, M., Chaker, N. (eds) Fuzzy Control. Advances in Soft Computing, vol 6. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1841-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1841-3_34
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1327-2
Online ISBN: 978-3-7908-1841-3
eBook Packages: Springer Book Archive