Graph planarization employing a harmony theory artificial neural network

Neural Computing and Applications

Volumn 6, Issue 2, 1997, Pages 116-124

  Tambouratzis, T ^a  

^a INSTITUTE OF NUCLEAR TECHNOLOGY AND RADIATION PROTECTION   (Greece)

Author keywords

Accuracy; Construction dependent solution; Graph planarization; Harmony theory; Transparency; Versatility

Indexed keywords

EID: 0031506844     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF01414008     Document Type: Article

References (7)

1. Smolensky P. Information processing in dynamical systems: foundations of harmony theory. In: Rumelhart DE, McClelland JL (Eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol 1. MIT Press, Cambridge, MA, 1986, pp. 184-281
2. Takefuji Y, Lin CW, Lee KC. A parallel algorithm for estimating the secondary structure in ribonucleic acids. Biol Cybern 1990; 63: 337-340
3. 2) algorithms for graph planarization. IEEE Trans CAD 1989; 8: 257-267
4. Takefuji T, Lee KC. A near-optimum parallel planarisation algorithm. Science 1989; 245: 1221-1223
5. Cimikowski R, Shope P. A neural-network algorithm for a graph layout problem. IEEE Trans Neural Networks 1996; 7: 341-345
6. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci 1985; 79: 2554-2558
7. Hinton GE, Sejnowski TJ. Learning and relearning in Boltzmann machines. In: Rumelhart DE, McClelland JL (Eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol 1. MIT Press, Cambridge, MA, 1986, pp. 282-317