A Study of Associative Memories with Hopfield neural network Model for handwritten character recognition

Sandeep Kumar, Manu Pratap Singh

Abstract


Neural network is the most important model which has been studied in past decades by
several researchers. Hopfield model is one of the network model proposed by J.J.
Hopfield that describes the organization of neurons in such a way that they function as
associative memory or also called content addressable memory. This is a recurrent
network similar to recurrent layer of the hamming network but which can effectively
perform the operation of both layer hamming network. The design of recurrent network
has always been interesting problems to research and a lot of work is going on present
application. In present paper we will discuss about the design of Hopfield Neural
Network (HNNs), bidirectional associative memory (BAMs) and multidirectional
associative memory (MAMs) for handwritten characters recognition. Recognized
characters are Hindi alphabets.

Keywords


Bidirectional associative memory (BAMs); and Multidirectional associative, memory (MAMs). ; Hopfield, Neural Network (HNNs); identifying hand-writtencharacters;.

Full Text:

PDF


DOI: http://dx.doi.org/10.29218/srmsjoms.v3i01.10876

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 SRMS Journal of Mathematical Sciences

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.