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A weight smoothing algorithm is proposed in this paper to improve a neural network's generalization capability. The algorithm can be used when the data patterns to be classified are presented on an n-dimensional grid (n>/=1) and there exists some correlations among neighboring data points within a pattern. For a fully-interconnected feedforward net, no such correlation information is embedded into the architecture. Consequently, the correlations can only be extracted through sufficient amount of network training. With the proposed algorithm, a smoothing constraint is incorporated into the objective function of backpropagation to reflect the neighborhood correlations and to seek those solutions that have smooth connection weights. Experiments were performed on problems of waveform classification, multifont alphanumeric character recognition, and handwritten numeral recognition. The results indicate that (1) networks trained with the algorithm do have smooth connection weights, and (2) they generalize better.

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Based on a linear ordering of vertices in a directed graph, a linear-time partitioning algorithm for parallel logic simulation is presented. Unlike most other partitioning algorithms, the proposed algorithm preserves circuit concurrency by assigning to processors circuit gates that can be evaluated at about the same time. As a result, the concurrency preserving partitioning (CPP) algorithm can provide better load balancing throughout the period of a parallel simulation. This is especially important when the algorithm is used together with a Time Warp simulation where a high degree of concurrency can lead to fewer rollbacks and better performance. The algorithm consists of three phases, and three conflicting goals can be separately considered in each phase so to reduce computational complexity. A parallel gate-level circuit simulator is implemented on an Intel Paragon machine to evaluate the performance of the CPP algorithm. The results are compared with two other partitioning algorithms to show that reasonable speedup may be achieved with the algorithm.