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The authors propose the first parallel improvement algorithm using the maximum neural network model for the bipartite subgraph problem. The goal of this NP-complete problem is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. A large number of instances have been simulated to verify the proposed(More)
Multi-layer channel routing is one of cumbersome jobs in automatic layout design of VLSI chips and PCBs. As VLSI chips have been used in every field of electrical engineering , it becomes more important to reduce the layout design time. With the advancement of the VLSI technology, four-layer problems can be treated and the algorithms for more than(More)
The application of the Hopfield neural network for the multispectral unsupervised classification of MR images is reported. Winner-take-all neurons were used to obtain a crisp classification map using proton density-weighted and T(2)-weighted images in the head. The preliminary studies indicate that the number of iterations needed to reach ;good' solutions(More)
A parallel and stochastic version of Hopfield-like neural networks is presented. Cauchy color noise is assumed. The specific noise is desirable for fast convergence to a fixed point representing a neighborhood minimum. It can be quickly quenched at each iteration according to a proven cooling schedule in generating random states on the energy landscape. An(More)
A parallel algorithm for finding a near-maximum independent set in a circle graph is presented. An independent set in a graph is a set of vertices, no two of which are adjacent. A maximum independent set is an independent set whose cardinality is the largest among all independent sets of a graph. The algorithm is modified for predicting the secondary(More)
For Part I, see ibid., p.275-82. The authors introduce a neural computation architecture based on a stochastic Hopfield neural network model for solving job-shop scheduling. A computation circuit computes the total completion times (costs) of all jobs, and the cost difference is added to the energy function of the stochastic neural network. Using a(More)
An application of neural networks is presented for solving job-shop scheduling, and NP-complete optimization problem with constraint satisfaction. In particular, the authors introduce a neural computation architecture based on a stochastic Hopfield neural-network model. First, the job-shop problem is mapped into a two-dimensional matrix representation of(More)