采用神经网络方法建立了重轨生产性能预报模型，并通过模型结构优化提高了模型预报的可靠性。
A mathematic model to predict the mechanical properties of heavy rail steel has been developed by means of neural network according to the demand of production.
XMLNSC 验证的性能也在相当大的程度上胜过 MRM 验证，因为新的高性能 XML 解析器的体系结构针对模型驱动的场景进行了优化。
XMLNSC validation considerably out-performs MRM validation too, as the architecture of the new high performance XML parser is optimized for the model-driven scenario.
本文运用线性规划理论，建立了一个包括54个变量(产品)，43个约束条件产品结构优化数学模型。
A mathematics model which deals with 54 variables and 43 constraint conditions is proposed to optimize the product structure by using linear programming theory.
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