Abstract
Different from some early learning algorithms such as backpropagation (BP) or radial basis function (RBF) algorithms, a new data driven algorithm for training neural networks is proposed. The new data driven methodology for training feedforward neural networks means that the system modeling are performed directly using the input-output data collected from real processes, To improve the efficiency, the parallel computation method is introduced and the performance of parallel computing for the new data driven algorithm is analyzed. The results show that, by using the parallel computing mechanisms, the training speed can be much higher.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ampazis, N., Perantonis, S.J.: Two highly efficient second-order algorithms for training feedforward networks. IEEE Trans. Neural Netw. 13, 1064–1073 (2002)
Khashman, A.: A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients. IEEE Trans. Neural Netw. 19, 1896–1909 (2008)
Bortman, M., Aladjem, M.: A Growing and Pruning Method for Radial Basis Function Networks. IEEE Trans. Neural Netw. 20, 1039–1045 (2009)
Wedge, D., Ingram, D., McLean, D., Mingham, C., Bandar, Z.: On global-local artificial neural networks for function approximation. IEEE Trans. Neural Netw. 17, 942–952 (2006)
Zhang, D.Y.: New Theories and Methods on Neural Networks. Tsinghua University Press, Beijing (2006) (in Chinese)
Zhang, D.Y.: New Algorithm for Training Feedforward Neural Networks with Cubic Spline weight functions. Systems Engineering and Electronics 28, 1434–1437 (2006) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D. (2013). Parallel Computation of a New Data Driven Algorithm for Training Neural Networks. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)