Abstract
The discrete cosine transform (DCT) is commonly known in signal processing. In this paper DCT is used in computational intelligence to show its usefulness. Proposed DCT method is used to reduce the size of system which results in faster processing with limited and controlled precision lost. Proposed method is compared to other ones like Fuzzy Systems, Neural Networks, Support Vector Machines, etc. to investigate the ability to solve sample problem. The results show that the method can be successfully used and the results are comparable or better to those achieved by other methods considered as powerful ones.
This work was supported by the National Science Centre, Krakow, Poland, under grant No.2015/17/B/ST6/01880.
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References
Greblicki, W., Pawlak, M.: Fourier and hermite series estimates of regression functions. Ann. Inst. Stat. Math. 37(1), 443–454 (1985)
Rutkowski, L., Rafajlowicz, E.: On optimal global rate of convergence of some nonparametric identification procedures. IEEE Trans. Autom. Control 34(10), 1089–1091 (1989)
Rafajlowicz, E., Pawlak, M.: On function recovery by neural networks based on orthogonal expansions. Nonlinear Anal. Theory Methods Appl. 30(3), 1343–1354 (1997)
Kayacan, E., Kayacan, E., Khanesar, M.A.: Identification of nonlinear dynamic systems using type-2 fuzzy neural networks a novel learning algorithm and a comparative study. IEEE Trans. Ind. Electron. 62(3), 1716–1724 (2015)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC–15(1), 116–132 (1985)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1127 (2009). Also published as a book. Now Publishers
Dahl, G.E., Ranzato, M., Mohamed, A., Hinton, G.E.: Phone recognition with the mean-covariance restricted boltzmann machine. In: NIPS2010
Wu, X., Rozycki, P., Wilamowski, B.: Hybride constructive algorithm for single layer feeforward network learning. IEEE Trans. Neural Netw. Learn. Syst. 26(8), 1659–1668 (2015)
Wilamowski, B.M.: Neural network architectures and learning algorithms- how not to be frustrated with neural networks. IEEE Ind. Electron. Mag. 3(4), 56–63 (2009)
Wilamowski, B.M., Yu, H.: Improved computation for levenberg marquardt training. IEEE Trans. Neural Netw. 21(6), 930–937 (2010)
Tiantian, X., Hao, Y., Hewlett, J., Rozycki, P., Wilamowski, B.: Fast and efficient second-order method for training radial basis function networks. IEEE Trans. Neural Netw. Learn. Syst. 23(4), 609–619 (2012)
Lang, K.L., Witbrock, M.J.: Learning to tell two spirals apart. In: Proceedings of the 1988 Connectionists Models Summer School. Morgan Kaufman (1988)
Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems 2, pp. 524–532. Morgan Kaufmann, San Mateo (1990)
Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw. 5(6), 989–993 (1994)
Demuth, H.B., Beale, M.: Neural Network Toolbox: For Use with MATLAB. Mathworks, Natick (2000)
Różycki, P., Kolbusz, J., Bartczak, T., Wilamowski, B.M.: Using parity-N problems as a way to compare abilities of shallow, very shallow and very deep architectures. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 112–122. Springer, Cham (2015). doi:10.1007/978-3-319-19324-3_11
Hunter, D., Hao, Y., Pukish, M.S., Kolbusz, J., Wilamowski, B.M.: Selection of proper neural network sizes and architecturesa comparative study. IEEE Trans. Ind. Inform. 8, 228–240 (2012)
Smola, A.J., Schlkopf, B.: A tutorial on support vector regression. NeuroCOLT2 Technical report NC2-TR-1998-030 (1998)
Yu, H., Xie, T., Hewlett, J., Rozycki, P., Wilamowski, B.M.: Fast and efficient second order method for training radial basis function networks. IEEE Trans. Neural Netw. 24(4), 609–619 (2012)
Cecati, C., Kolbusz, J., Siano, P., Rozycki, P., Wilamowski, B.: A novel RBF training algorithm for short-term electric load forecasting: comparative studies. IEEE Trans. Ind. Electron. 62(10), 6519–6529 (2015)
Rao, K., Yip, P.: Discrete Cosine Transform: Algorithms, Advantages, Applications. Academic Press, Boston (1990). ISBN0-12-580203-X
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Olejczak, A., Korniak, J., Wilamowski, B.M. (2017). Discrete Cosine Transformation as Alternative to Other Methods of Computational Intelligence for Function Approximation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_13
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