An Expansion of the Neural Network Theory by Introducing Hebb Postulate
Volume 21, Issue 3 (2010), pp. 339–348
Pub. online: 1 January 2010
Type: Research Article
Received
1 June 2009
1 June 2009
Accepted
1 April 2010
1 April 2010
Published
1 January 2010
1 January 2010
Abstract
In the presented paper, some issues of the fundamental classical mechanics theory in the sense of Ising physics are introduced into the applied neural network area. The expansion of the neural networks theory is based primarily on introducing Hebb postulate into the mean field theory as an instrument of analysis of complex systems. Appropriate propositions and a theorem with proofs were proposed. In addition, some computational background is presented and discussed.