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

abstract

• 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.

authors

• Huang, Tingwen

published proceedings

• ADVANCES IN NEURAL NETWORKS - ISNN 2014

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

• January 2014

publisher

• Springer Nature  Publisher

published in

• Lecture Notes in Artificial Intelligence  Journal

keywords

• Complex-valued Radial Basis Function Neural Networks
• Filtering Center
• Random Traversal Process
• Repeat Weighted Boosting Search

Digital Object Identifier (DOI)

• 10.1007/978-3-319-12436-0_41

International Standard Book Number (ISBN) 13

• 978-3-319-12435-3

start page

• 366

end page

• 373

volume

• 8866

URL

• http://dx.doi.org/10.1007/978-3-319-12436-0_41