Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2004-02-04
Physics
Condensed Matter
Disordered Systems and Neural Networks
13 pages, 4 figures
Scientific paper
10.1143/JPSJ.73.2406
Bidirectional associative memory (BAM) is a kind of an artificial neural network used to memorize and retrieve heterogeneous pattern pairs. Many efforts have been made to improve BAM from the the viewpoint of computer application, and few theoretical studies have been done. We investigated the theoretical characteristics of BAM using a framework of statistical-mechanical analysis. To investigate the equilibrium state of BAM, we applied self-consistent signal to noise analysis (SCSNA) and obtained a macroscopic parameter equations and relative capacity. Moreover, to investigate not only the equilibrium state but also the retrieval process of reaching the equilibrium state, we applied statistical neurodynamics to the update rule of BAM and obtained evolution equations for the macroscopic parameters. These evolution equations are consistent with the results of SCSNA in the equilibrium state.
Kido Shoji
Okada Masato
Shouno Hayaru
No associations
LandOfFree
Analysis of Bidirectional Associative Memory using SCSNA and Statistical Neurodynamics does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Analysis of Bidirectional Associative Memory using SCSNA and Statistical Neurodynamics, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Analysis of Bidirectional Associative Memory using SCSNA and Statistical Neurodynamics will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-565068