Literature DB >> 2025667

Markovian neural networks.

M Kovacic1.   

Abstract

The neural network that efficiently and nearly optimally solves difficult optimization problems is defined. The convergence proof for the Markovian neural network that asynchronously updates its neurons' states is also presented. The comparison of the performance of the Markovian neural network with various combinatorial optimization methods in two domains is described. The Markovian neural network is shown to be an efficient tool for solving optimization problems.

Mesh:

Year:  1991        PMID: 2025667     DOI: 10.1007/bf00199598

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  4 in total

1.  Optimization by simulated annealing.

Authors:  S Kirkpatrick; C D Gelatt; M P Vecchi
Journal:  Science       Date:  1983-05-13       Impact factor: 47.728

2.  On the stability of the Travelling Salesman Problem algorithm of Hopfield and Tank.

Authors:  G V Wilson; G S Pawley
Journal:  Biol Cybern       Date:  1988       Impact factor: 2.086

3.  Solving a combinatorial problem via self-organizing process: an application of the Kohonen algorithm to the traveling salesman problem.

Authors:  J C Fort
Journal:  Biol Cybern       Date:  1988       Impact factor: 2.086

4.  "Neural" computation of decisions in optimization problems.

Authors:  J J Hopfield; D W Tank
Journal:  Biol Cybern       Date:  1985       Impact factor: 2.086

  4 in total

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