INTERPOLATING NEURO-FUZZY NETWORK FOR MODELING COLOR RENDERING OF A PRINTING SYSTEM

Authors

  • E. V. Bodyansky Kharkiv National University of Radio Electronics, Ukraine
  • N. E. Kulishova Kharkiv National University of Radio Electronics, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2007-2-18

Abstract

The architecture and learning algorithm for the neurofuzzy system is proposed. This system is aimed for decision of the interpolation task of two-variable functions, that are known in nodes, which are arbitrary placed on the plane.

Author Biographies

E. V. Bodyansky, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor

N. E. Kulishova, Kharkiv National University of Radio Electronics

Ph.D., Associate Professor

References

Bishop C. M. Neural Networks for Pattern Recognition. – Oxford: Clarendon Press, 1995. – 482 p.

Hristev R. M. The ANN Book. – 1998. – 374 p.

Sigitani T., Iigumi G., Maeda H. Image interpolation for progressive transmission by using radial-basis functions networks // IEEE Trans. on Neural Networks. – 1999. – 10. – Pp. 381–390.

Kulishova N. Ye. Interpolyatsiya koeffitsiyentov otrazheniya krasok s pomoshch'yu radial'no-bazisnoy iskusstvennoy neyronnoy seti // Bionika intellekta. – 2006. – № 1(64). – S.

Jang J. – S.R., Sun C. – T., Mizutani E. Neuro-Fuzzy and Soft Computing. – Upper Saddle River, NJ: Prentice Hall, 1997. – 614 p.

Cios K. J., Pedrycz W. Neuro-fuzzy algorithms / In: «Handbook on Neural Computation». – Oxford: IOP Publishing and Oxford University Press, 1997. – D1.3:1–D1.3: 7.

Jang J. – S.R. ANFIS: Adaptive-Network-based Fuzzy Inference System // IEEE Trans. on Systems, Man and Cybernetics. – 1993. – 23. – Pp. 665–685.

Jang J. – S.R., Sun C. – T., Mizutani E. Neuro-fuzzy modeling and control // Proc. IEEE. – 1995. – 83. – Pp. 378–406.

Brown M., Harris C. J. Neural networks for modeling and control / In: Ed. by C. J. Harris «Advances in Intellectual Control». – London: Taylor and Francis, 1994. – Pp. 17–55.

Specht D. A general regression neural network // IEEE Trans. on Neural Networks. – 1991. – 2. – Pp. 568–576.

Wang H., Liu G. P., Harris C. J., Brown M. Advanced Adaptive Control. – Oxford: Pergamon, 1995. – 262 p.

Wang L., Mendel J. M. Fuzzy basis functions, universal approximation and orthogonal least squares learning // IEEE Trans. on Neural Networks. – 1992. – 3. – Pp. 807–814.

Wang L. – X., Mendel J. M. Generating fuzzy rules by learning from examples // IEEE Trans. on Systems, Man and Cybernetics. – 1992. – 22. – Pp. 1414–1427.

Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control // IEEE Trans. on Systems, Man and Cybernetics. – 1985. – 15. – Pp. 116–132.

Zahirniak D. R., Chapman R., Rogers S. K., Suter B. W., Kabrisky M., Pyati V. Pattern recognition using radial basis function network // Proc. 6-th Ann. Aerospace Application of AI Conf. – Dayton, OH, 1990. – Pp. 249–260.

Parzen E. On the estimation of a probability density function and the mode // Ann. Math. Statist. – 1962. – 38. – Pp. 1065–1076.

Nadaraya E. A. O neparametricheskikh otsenkakh plotnosti veroyatnosti i regressii // Teoriya veroyatnostey i yeye primeneniye. – 1965. – 10. – № 1. – S. 199–203.

Friedman J., Hastie J., Tibshirani R. The Elements of Statistical Learning, Data Mining, Inference, and Prediction. – Berlin: Springer, 2003. – 552 p.

Published

2024-11-19

How to Cite

Bodyansky, E. V., & Kulishova, N. E. (2024). INTERPOLATING NEURO-FUZZY NETWORK FOR MODELING COLOR RENDERING OF A PRINTING SYSTEM. Radio Electronics, Computer Science, Control, (2), 90. https://doi.org/10.15588/1607-3274-2007-2-18

Issue

Section

Neuroinformatics and intelligent systems