Large-scale economic dispatch using an improved Hopfield neural network

1997 ◽  
Vol 144 (2) ◽  
pp. 181 ◽  
Author(s):  
T. Yalcinoz ◽  
M.J. Short
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Economic Dispatch ◽  
Hopfield Neural Network

scholarly journals Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Corentin Delacour ◽  
Aida Todri-Sanial
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Hebbian Learning ◽  
Hopfield Neural Network ◽  
Analytical Framework ◽  
Relaxation Oscillators ◽  
Oscillatory Neural Network ◽  
Network Functionality ◽  
Fully Connected ◽  
Neuromorphic Architecture

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

Economic Dispatch Using an Enhanced Hopfield Neural Network

2008 ◽  
Vol 36 (7) ◽  
pp. 719-732 ◽  
Author(s):  
A. Y. Abdelaziz ◽  
S. F. Mekhamer ◽  
M. A. L. Badr ◽  
M. Z. Kamh
Keyword(s):  
Neural Network ◽  
Economic Dispatch ◽  
Hopfield Neural Network

Experimental Demonstration of Hopfield Neural Network using DNA molecules

2011 ◽  
Vol 1346 ◽  
Author(s):  
Hayri E. Akin ◽  
Dundar Karabay ◽  
Allen P. Mills ◽  
Cengiz S. Ozkan ◽  
Mihrimah Ozkan
Keyword(s):  
Neural Network ◽  
Data Storage ◽  
Large Scale ◽  
Fault Tolerant ◽  
Hamiltonian Path ◽  
Hopfield Neural Network ◽  
Dna Molecules ◽  
Processing Power ◽  
Dna Strands ◽  
Molecular Reactions

ABSTRACTDNA Computing is a rapidly-developing interdisciplinary area which could benefit from more experimental results to solve problems with the current biological tools. In this study, we have integrated microelectronics and molecular biology techniques for showing the feasibility of Hopfield Neural Network using DNA molecules. Adleman’s seminal paper in 1994 showed that DNA strands using specific molecular reactions can be used to solve the Hamiltonian Path Problem. This accomplishment opened the way for possibilities of massively parallel processing power, remarkable energy efficiency and compact data storage ability with DNA. However, in various studies, small departures from the ideal selectivity of DNA hybridization lead to significant undesired pairings of strands and that leads to difficulties in schemes for implementing large Boolean functions using DNA. Therefore, these error prone reactions in the Boolean architecture of the first DNA computers will benefit from fault tolerance or error correction methods and these methods would be essential for large scale applications. In this study, we demonstrate the operation of six dimensional Hopfield associative memory storing various memories as an archetype fault tolerant neural network implemented using DNA molecular reactions. The response of the network suggests that the protocols could be scaled to a network of significantly larger dimensions. In addition the results are read on a Silicon CMOS platform exploiting the semiconductor processing knowledge for fast and accurate hybridization rates.

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