Abstract
In this paper, as an extension of real-valued orthogonal least-squares regression with tunable kernels (OLSRTK), a complex-valued OLSRTK is presented which can be used to construct a suitable sparse regression model. In order to enhance the real-valued OLSRTK, the random traversal process and method of filtering center are adopted in complex-valued OLSRTK. Then, the complex-valued OLSRTK is applied to train complex-valued radial basis function neural networks. Numerical results show that better performance can be achieved by the developed algorithm than by the original real-valued OLSRTK.
The work was supported by the National Natural Science Foundation of China under Grant No. 61005047, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK2010214. Also, this publication was made possible by NPRP grant #4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s].
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Mo, X., Huang, H., Huang, T. (2014). An Improved Learning Algorithm with Tunable Kernels for Complex-Valued Radial Basis Function Neural Networks. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_41
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DOI: https://doi.org/10.1007/978-3-319-12436-0_41
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