Managing spatio-temporal complexity in Hopfield neural network simulations for large-scale static optimization.

DOI10.1016/J.MATCOM.2003.09.023zbMath1039.65049OpenAlexW2006781625MaRDI QIDQ1427733

Gursel Serpen

Publication date: 14 March 2004

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2003.09.023

zbMATH Keywords

optimizationcomputational complexitynumerical examplestraveling salesman problemHopfield neural networktime complexityartificial neural networkcombinatorial optimization problemspace complexityweight matrixlarge-scale simulationhuge data array

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Transportation, logistics and supply chain management (90B06) Combinatorial optimization (90C27) Complexity and performance of numerical algorithms (65Y20)

Cites Work

• Unnamed Item
• ``Neural computation of decisions in optimization problems
• Determination of weights for relaxation recurrent neural networks
• Collective Computation With Continuous Variables
• Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research
• An improved branching rule for the symmetric travelling salesman problem
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.

This page was built for publication: Managing spatio-temporal complexity in Hopfield neural network simulations for large-scale static optimization.