SOM based analysis of pulping process data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1607, Issue , 1999, Pages 568-577

  Simula, Olli ^a   Alhoniemi, Esa ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; COMPLEX NETWORKS; CONFORMAL MAPPING; NEURAL NETWORKS; PULP DIGESTERS; SELF ORGANIZING MAPS; STEELMAKING; TELECOMMUNICATION INDUSTRY; TELECOMMUNICATION NETWORKS; VISUALIZATION;

CONTINUOUS-PULP DIGESTER; DATA-DRIVEN ANALYSIS; ENGINEERING APPLICATIONS; HIGH DIMENSIONAL DATA; NEURAL NETWORK ALGORITHM; NONLINEAR MAPPINGS; TWO-DIMENSIONAL GRIDS; VISUAL PRESENTATION;

DATA VISUALIZATION;

DATA; DIGESTERS; PULPING; TELECOMMUNICATIONS;

EID: 38649128250     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/BFb0100524     Document Type: Conference Paper

References (16)

1. E. Alhoniemi, J. Hollmén, O. Simula, and J. Vesanto. Process monitoring and Modeling Using the Self-Organizing Map. Integrated Computer-Aided Engineering, 6(1):3-14, 1999.
2. B.S. Dayal, J. F. MacGregor, P. A. Taylor, and S. Marcikic. Application of feedforward neural networks and partial least squares for modelling kappa number in a continuous kamyr digester. Pulp & Paper Canada, 95(1):26-32, 1994.
3. R. R. Gustafson, C. A. Sleicher, W. T. McKean, and B. A. Finlayson. Thoretical model of thekraft pulping process. Industrial & Engineering Chemistry Process, 22(1):87-96, Jan. 1983.
4. J. Himberg. Enhancing SOM-based data visualization by linking different data projections. In L. Xu, L. W. Chan, and I. King, editors, Intelligent Data Engineering and Learning, pages 427-434. Springer, 1998.
5. E. Häkänen. A mathematical model for two-phase flow in a continuous digester. Tappi Journal, 70:122-126, Dec. 1987.
6. S. Kaski, J. Venna, and T. Kohonen. Tips for Processing and Color-Coding of Self- Organizing Maps. In G. Deboeck and T. Kohonen, editors, Visual Explorations in Finance, Springer Finance, chapter 14, pages 195-202. Springer-Verlag, 1998.
7. T. Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer, Berlin, Heidelberg, 1995.
8. T. Kohonen, E. Oja, O. Simula, A. Visa, and J. Kangas. Engineering Applications of the Self-Organizing Map. Proceedings of the IEEE, 84(10):1358-1384, 1996.
9. M. T. Musavi, D. It. Coughglin, and M. Qiao. Prediction of wood pulp k# with radial basis function neural network. In Proceedings of the 1995 IEEE International Symposium on Circuits and Systems, volume 3, pages 1716-1719, Piscataway, 1995. IEEE.
10. K. Ralvio, J. Hcnriksson, and O. Simula. Neural detection of QAM signal with strongly nonlinear receiver. Neurocomputing, 21:159-171, 1998.
11. J. B. Rudd. Prediction and control of pulping processes using neural network models. In 80th Annual Meeting, Technical Section, volume B, pages 169-173, Montreal, Quebec, Canada, Feb. 1994. Canadian Pulp & Paper Association.
12. O. Simula and J. Kangas. Neural Networks for Chemical Engineers, volume 6 of Computer-Aided Chemical Engineering, chapter 14: Process monitoring and visualization using self-organizing maps, pages 371-384. Elsevier, Amsterdam, 1995.
13. H. Tang and O. Simula. The optimal utilization of multi-service scp. In Intelligent Networks and New Technologies, pages 175-188. Chapman & Hall, 1996.
14. J. Vesanto. SOM-Based Data Visualization Methods. Intelligent Data Analysis, 1998. Accepted for publication.
15. J. Vesanto and J. Ahola. Hunting for Correlations in Data Using the Self-Organizing Map. Accepted for publication in International ICSC Symposium on Advances in Intelligent Data Analysis.
16. J. Vesanto, J. Himberg, M. Siponen, and O. Simula. Enhancing SOM Based Data Visualization. In T. Yamakawa and G. Matsumoto, editors, Proceedings of the 5th International Conference on Soft Computing and Information/Intelligent Systems, pages 64-67. World Scientific, 1998.