A Weakly Connected Memristive Neural Network for Associative Memory

abstract

• 2013, Springer Science+Business Media New York. In this paper, we propose a star-like weakly connected memristive neural network which is organized in such a way that each cell only interacts with the central cells. By using the describing function method and Malkins theorem the phase deviation of this dynamical network is obtained. And then, under the Hebbian learning rule the phase deviation is designed as a desired model for associative memory. Moreover, we take the store and recall of digital images as an example to demonstrate the performance of associative memory. The main contribution of this paper is supply a useful mechanism which the new potential circuit element memristor can be used to realize the associative.

authors

• Huang, Tingwen

published proceedings

• NEURAL PROCESSING LETTERS

author list (cited authors)

• Wang, X., Li, C., Huang, T., & Duan, S.

citation count

• 2

complete list of authors

• Wang, Xin||Li, Chuandong||Huang, Tingwen||Duan, Shukai

publication date

• December 2014

publisher

• Springer Nature  Publisher

published in

• Neural Processing Letters  Journal

keywords

• Associative Memory
• Malkin's Theorem
• Memristor
• Neural Networks

Digital Object Identifier (DOI)

• 10.1007/s11063-013-9328-3

start page

• 275

end page

• 288

volume

• 40

issue

• 3

URL

• http://dx.doi.org/10.1007/s11063-013-9328-3