Complex Scheduling with Potts Neural Networks

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Standard

Complex Scheduling with Potts Neural Networks. / Gislén, Lars; Peterson, Carsten; Söderberg, Bo.

I: Neural Computation, Vol. 4, Nr. 6, 1992, s. 805-831.

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Harvard

APA

CBE

MLA

Vancouver

Author

Gislén, Lars ; Peterson, Carsten ; Söderberg, Bo. / Complex Scheduling with Potts Neural Networks. I: Neural Computation. 1992 ; Vol. 4, Nr. 6. s. 805-831.

RIS

TY - JOUR

T1 - Complex Scheduling with Potts Neural Networks

AU - Gislén, Lars

AU - Peterson, Carsten

AU - Söderberg, Bo

PY - 1992

Y1 - 1992

N2 - In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved efficiently with high quality solutions as results.

AB - In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved efficiently with high quality solutions as results.

U2 - 10.1162/neco.1992.4.6.805

DO - 10.1162/neco.1992.4.6.805

M3 - Article

VL - 4

SP - 805

EP - 831

JO - Neural Computation

JF - Neural Computation

SN - 1530-888X

IS - 6

ER -