• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.05a
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• pp.225-229
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• 2008

We present an intelligent forging simulator AFDEX. The intelligent forging simulator is determined by the adaptive and optimal mesh generation technique and many intelligent application-oriented special functions which minimize the user-intervention during forging simulation. Of course, the solution accuracy should be optimized in the intelligent simulation. We have developed AFDEX to meet the requirement on intelligent simulation. Its characteristics are introduced with the help of typical application examples.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.