Neural computing for optimization and combinatorics by editors, Yoshiyasu Takefuji, Jun Wang.

By editors, Yoshiyasu Takefuji, Jun Wang.

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C. " IEEE Trans, on Neural Networks, vol. 1, no. I , pp. 143-145, March 1990. [25] Y. Takefuji, C. W . L i n , and K. C. " Biological Cybernetics. 63. 5, pp. 337¬ 340. 1990. [26] Y. Takefuji, L i - l i n Chen. K. C. Lee and J. Huffman. " IEEE Trans, on Neural Networks, vol. 1 . no. 3. pp. 263-267. Sep. 1990. [27] Y . Takefuji and K. C. Lee. "A super parallel sorting algorithm based on neural Maximum Clique Problems: Pan I 5} networks" IEEE Trans, on Circuits and Systems, vol. 37, I I , pp. 1425-1429.

Coefficient-parameter tuning in the motion equation is not required in the maximum neural network while the conventional neural networks must suffer from it; 2) the equilibrium state of the maximum neural network is clearly defined in order to terminate the algorithm while the existing neural networks do not have the clear definition; and 3) the maximum neural network always allows the state o f the system to converge to the feasible solution while the existing neural networks cannot guarantee it.

In the detailed layout, in-gate wiring is performed along the vertical columns and between-gates connections are made horizontally. The width of each gate and the separation distance between two neighboring gates are ad hoc with respect to physical constraints. The total horizontal dimension of an array is considered fixed. The vertical tracks determine the total chip area which must be minimized. The problem of finding a minimum track placement can be analogous to the general layout compaction problem.

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