Integration of connectionist methods and chaotic time-series analysis for the prediction of process data

International Journal of Intelligent Systems

Volumn 13, Issue 6, 1998, Pages 519-538

  Kozma, R ^a   Kasabov, N K ^a   Kim, J S ^a   Cohen, A ^b  

^a UNIVERSITY OF OTAGO   (New Zealand)

^b Waste Solutions Ltd   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ARTIFICIAL INTELLIGENCE; CHAOS THEORY; DATA PROCESSING; FRACTALS; FUZZY SETS; ITERATIVE METHODS; LEARNING AIDS FOR HANDICAPPED PERSONS; STATISTICAL PROCESS CONTROL; TIME SERIES ANALYSIS;

CONNECTIONIST METHODS;

NEURAL NETWORKS;

EID: 0032098514     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1098-111X(199806)13:6<519::AID-INT7>3.0.CO;2-O     Document Type: Article

References (20)

1. G. Sugihara and R.M. May, "Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series," Nature, 344, 734-741 (1990).
2. M. Casdagli, "Nonlinear prediction of chaotic time series," Physica D, 35, 335-356 (1989).
3. J.C. Principle, A. Rathie, and J.M. Kuo, "Prediction of chaotic time series with neural networks and the issue of dynamic modeling," Int. J. Bifurcation Chaos, 2, 989-996 (1992).
4. J.S.R. Jang, "ANFIS: Adaptive-network-based fuzzy interference system," IEEE Trans. Syst. Man Cybernetics, 23, 665-684 (1993).
5. N.K. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, Cambridge, MA, 1996.
6. H. Szu and C. Szu, "Multiresolution analyses of fuzzy membership functions by chaotic neural networks," Methods Concept Design Appl. Intell. Syst., 1, 38-42 (1996).
7. N.K. Kasabov, "Adaptive connectionist production systems," Neurocomputing, 13, 95-117 (1996).
8. B.B. Mandelbrot, The Fractal Geometry of Nature, Freeman, New York, 1982.
9. T. Higuchi, "Approach to an irregular time series on the basis of the fractal theory," Physica D, 31, 277 (1988).
10. T. Higuchi, "Relationship between the fractal dimension and the power law index for a time series: A numerical investigation," Physica D, 46, 254 (1990).
11. M. Sakuma, R. Kozma, and M. Kitamura, "Characterization of anomalies by applying methods of fractal analysis," Nucl. Technol., 113, 86-99 (1996).
12. M.J. Embrechts and Y. Danon, "Determining the fractal dimension of a time series with a neural net," in Intelligent Engineering Systems through Artificial Neural Networks, C.H. Dagli, L.I. Burke, B.R. Fernandez, and J. Ghosh, Eds., ASME Press, New York, 1993, Vol. 3, pp. 897-902.
13. N.K. Kasabov and R. Kozma, Adaptive Fuzzy Neural Networks and Applications for Chaotic Time Series Analysis and Phoneme-Based Speech Recognition, unpublished.
14. N.K. Kasabov, R. Kozma, and M.J. Watts, "Optimization and adaptation of fuzzy neural networks through genetic algorithms and structural learning," Info. Sci. 8 (1998). To appear.
15. R. Reed, "Pruning algorithms - A survey," IEEE Trans. Neural Networks, 4, 740-747 (1993).
16. M. Ishikawa, "Structural learning with forgetting," Neural Networks, 9, 509-521 (1996).
17. R. Kozma, "On the accuracy of mapping by neural networks trained by backpropagation with forgetting," Neurocomputing, 13, 295-311 (1996).
18. D.A. Miller and J.M. Zurada, "Dynamics of structural learning with an adaptive forgetting rate," Proc. IEEE ICNN97, 1997, Vol. 4, pp. 1827-1832.
19. G. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden Day, San Francisco, 1970.
20. A. Cohen et al., "Application of computational intelligence for on-line control of a sequencing batch reactor at Morrinsville Sewage Treatment Plant," Proc. IAWQ Conf. Advance Wastewater Treatment, Sept. 1996.