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

  • Xia Mo
  • He Huang
  • Tingwen Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)

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.

Keywords

Complex-valued radial basis function neural networks Random traversal process Repeat weighted boosting search Filtering center 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringSoochow UniversitySuzhouPeople’s Republic of China
  2. 2.Texas A&M University at QatarDohaQatar

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