选取了几种常用的金属氧化物掺杂剂,在均匀实验结构的基础上用人工神经网络方法对掺杂PZT陶瓷的性能进行分析和优化.实验结果表明,掺杂PZT体系的人工神经网络模型要比多重非线形回归模型准确得多,而且以人工神经网络模型为指导对材料进行优化后的性能预测也比较准确,说明人工神经网络在陶瓷这种多组分固溶体材料的性能分析中具有良好的使用前景.
Artificial neural network (ANN) technique was applied to model the PZT based piezoelectric ceramics system. After selecting several dopants, the experimental results of 21 PZT
samples were analyzed by a BP network based on the homogenous experimental design. Calculated results indicated that the ANN model was much more accurate than multiple
nonlinear regression analysis (MNLR) model for the same set of data. Optimized formulations were also calculated and the optimized d33 and Kp output values agreed well with predicted values. These results suggest that the ANN based modeling is a very
useful tool in dealing with problems with serious non-linearity encountered in the property analysis of the complicated solid solution material.
参考文献
| [1] | Basheer I A, Hajmeer M. J. Micro. Meth., 2000, 43 (1): 3--31. [2] 郭栋, 王永力, 夏军涛, 等(Guo D, et al). 无机材料学(Journal of Inorganic Materials), 2002 17 (4): 845--851. [3] 方开泰. 均匀设计与均匀设计表. 北京: 科学出版社, 1994. [4] Rao S S. Optimization Theory and Applications, 2nd ed., New York: Halsted Press, 1984. |
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