An Improved Learning Algorithm with Tunable Kernels for Complex-Valued Radial Basis Function Neural Networks Conference Paper uri icon


  • Springer International Publishing Switzerland 2014. 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.

published proceedings


author list (cited authors)

  • Mo, X., Huang, H. e., & Huang, T.

citation count

  • 0

complete list of authors

  • Mo, Xia||Huang, He||Huang, Tingwen

publication date

  • November 2014