Minimum-seeking properties of analog neural networks with multilinear objective functions

DOI10.1109/9.402228zbMath0833.65056OpenAlexW2133838859MaRDI QIDQ4850480

Mathukumalli Vidyasagar

Publication date: 25 February 1996

Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/3998ccf080a83db2e5f365e72a1a07051a5f3dc1

zbMATH Keywords

neural networkdynamical systemnonlinear programminginterior-point methodsdiscrete optimizationmultilinear objective functions

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Linear programming (90C05) Neural networks for/in biological studies, artificial life and related topics (92B20) Existence theories for optimal control problems involving ordinary differential equations (49J15)

Related Items (5)

Comparison of convergence and stability properties for the state and output solutions of neural networks ⋮ Are analog neural networks better than binary neural networks? ⋮ Dynamical Analysis of Continuous Higher-Order Hopfield Networks for Combinatorial Optimization ⋮ Linear systems in a saturated mode and convergence as gain becomes large of asymptotically stable equilibrium points of neural nets ⋮ Continuous dynamical systems that realize discrete optimization on the hypercube

This page was built for publication: Minimum-seeking properties of analog neural networks with multilinear objective functions