Abstract
Digital recognition is an important aspect of computer model recognition. It has a very good prospect, as well as basis for post-processing. Discrete hopfield neural network simulates memory mechanism of biological neural network. Specifically, it first learns memory samples, then associates original figure according to noise figure to be identified. The paper identifys figure which have sufferd noise pollution with the use of discrete hopfield neural network. Finally, the issue puts forward certain important proposals.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ma, X.: Hopfield network case study. Computer Simulation (2003)
Bai, C.: Global Stability of Almost Periodic Solutions of Hopfield Nueral Networks with Neutral Time-Varying Delays (2008)
Wang, Y.: Improvement of image restoration for Hopfield networks. Computer Engineering (2007)
Zhang, Y.: Feedback-type associative memory neural network. Computer Engineering (2009)
He, H.: Offline handwritten numeral recognition theory and algorithms based on Hopfield. Communication Technology (2009)
Zhang, C.: Training of associative memory neural network. Journal of Automation (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, X. (2012). Research and Realization of Digital Recognition Based on Hopfield Neural Network. In: Zhao, M., Sha, J. (eds) Communications and Information Processing. Communications in Computer and Information Science, vol 289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31968-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-31968-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31967-9
Online ISBN: 978-3-642-31968-6
eBook Packages: Computer ScienceComputer Science (R0)