Improved back-propagation algorithm for neural network training

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Recently, Artificial Neural Networks (ANNs) have become popular because they can learn complex mappings from the input/output data and are relatively easy to implement in any application. Although, a disadvantageous aspect of their usage is that they need (usually a significant amount of) time to be trained, which scales with the structural parameters of the networks and with the quantity of the input data. However, the training can be done offline; it has a non-negligible cost and further, can cause a delay in the operation. Fuzzy Neural Networks (FNNs) are the combinations of ANNs and fuzzy logic in order to incorporate the advantages of both methods (the learning ability of ANNs and the thinking ability of fuzzy logic). FNNs have fuzzy values in their weight parameters and in the output of each neuron. Circular Fuzzy Neural Networks (CFNNs) are FNNs with their topology realigned to a circular topology and the connections between the input layer and hidden layer trimmed. This may result in a dramatic reduction in the training time, while the precision and accuracy of the network are not affected. To further increase the speed of the training of the ANNs, FNNs, or CFNNs used for classification, a new training procedure is introduced in this paper: instead of directly using the training data in the training phase, the data is first clustered and the neural networks are trained by using only the centers of the obtained clusters.

Original languageEnglish
Title of host publicationWISP 2011 - IEEE International Symposium on Intelligent Signal Processing, Proceedings
Pages66-73
Number of pages8
DOIs
Publication statusPublished - Nov 16 2011
Event7th IEEE International Symposium on Intelligent Signal Processing, WISP 2011 - Floriana, Malta
Duration: Sep 19 2011Sep 21 2011

Publication series

NameWISP 2011 - IEEE International Symposium on Intelligent Signal Processing, Proceedings

Other

Other7th IEEE International Symposium on Intelligent Signal Processing, WISP 2011
CountryMalta
CityFloriana
Period9/19/119/21/11

Keywords

  • artificial neural networks
  • circular fuzzy neural networks
  • classification
  • clustering
  • fuzzy neural networks
  • reinforced learning
  • supervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing

Fingerprint Dive into the research topics of 'Improved back-propagation algorithm for neural network training'. Together they form a unique fingerprint.

  • Cite this

    Várkonyi-Kóczy, A. R., & Tusor, B. (2011). Improved back-propagation algorithm for neural network training. In WISP 2011 - IEEE International Symposium on Intelligent Signal Processing, Proceedings (pp. 66-73). [6051720] (WISP 2011 - IEEE International Symposium on Intelligent Signal Processing, Proceedings). https://doi.org/10.1109/WISP.2011.6051720