Hopfield Neural Network Learning Using Direct Gradient Descent of Energy Function

Zheng TANG  Koichi TASHIMA  Hirofumi HEBISHIMA  Okihiko ISHIZUKA  Koichi TANNO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E79-A    No.2    pp.258-261
Publication Date: 1996/02/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Neural Networks
neural networks,  gradient descent learning,  Hopfield model,  analog-to-digital conversion,  associative memory,  

Full Text: PDF>>
Buy this Article

A direct gradient descent learning algorithm of energy function in Hopfield neural networks is proposed. The gradient descent learning is not performed on usual error functions, but the Hopfield energy functions directly. We demonstrate the algorithm by testing it on an analog-to-digital conversion and an associative memory problems.