Optimization with Neural Networks

Summary, in Swedish

The recurrent neural network approach to combinatorial optimization has during the last decade evolved into a competitive and versatile heuristic method, that can be used on a wide range of problem types. In the state-of-the-art neural approach the discrete elementary decisions (not necessarily binary) are represented by continuous Potts mean-field neurons, interpolating between the available discrete states, with a dynamics based on iteration of a set of mean-field equations. Driven by annealing in an artificial temperature, they will converge into a candidate solution.

• Författare: Bo Söderberg
• Redaktör: J. W. Clark; T. Lindenau; M. L. Ristig
• Avdelning/ar: Beräkningsbiologi och biologisk fysik - Har omorganiserats
• Publiceringsår: 1999
• Språk: Engelska
• Sidor: 243-256
• Publikation/Tidskrift/Serie: Lecture Notes in Physics
• Volym: 522
• Länkar: https://www.springer.com/la/book/9783662142356
• Dokumenttyp: Del av eller Kapitel i bok
• Förlag: Springer
• Ämne: Computational Mathematics
• Aktiv: Published