We explore the applications of binary neurons with analog conductance or communication links in solving large-scale NP-complete optimization problems such as the classical traveling salesperson problems (TSP) and job-shop scheduling. In particular, the energy function of J.J. Hopfield and D.W. Tank neural network model is reformulated so that the network is likely to converge to a proportional number of valid solutions as the size of problem scales up. G.V. Wilson and G.S. Pawley identified the reasons for failure on the Hopfield and Tank computation algorithm in their attempts to solve a 10-city TSP. K. Sheff and H. Szu proposed a necessary and sufficient condition based on binary neurons and traceless energy for a Hopfield and Tank network to converge to stable states. In this paper, we study the effectiveness of this fast neural network convergent scheme through two case studies: an n-job m-machine job-shop problem and an N-city TSP.
|出版物ステータス||Published - 1988 1 1|
|イベント||International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA|
継続期間: 1988 9 6 → 1988 9 10
ASJC Scopus subject areas
- Cognitive Neuroscience
- Artificial Intelligence