Optimal and robust design of brain-state-in-a-box neural associative memories

DOI10.1016/J.NEUNET.2009.10.008zbMath1396.68101OpenAlexW2000103236WikidataQ48407121 ScholiaQ48407121MaRDI QIDQ1784543

Yonmook Park

Publication date: 27 September 2018

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.10.008

zbMATH Keywords

associative memorybrain-state-in-a-box neural networklinear matrix inequality problem

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Global stability of solutions to ordinary differential equations (34D23) Asymptotic properties of solutions to ordinary differential equations (34D05)

Related Items (3)

Sudoku associative memory ⋮ Towards a unified recurrent neural network theory: the uniformly pseudo-projection-anti-monotone net ⋮ Robust stability analysis of a class of neural networks with discrete time delays

Uses Software

• Matlab
• LMI toolbox
• Control System Toolbox

Cites Work

• Unnamed Item
• The brain-state-in-a-box neural model is a gradient descent algorithm
• Stability and optimization analyses of the generalized brain-state-in-a- box neural network model
• Analysis and synthesis of a class of neural networks: linear systems operating on a closed hypercube
• Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
• Linear Matrix Inequalities in System and Control Theory
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.

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