| 1. | Study on improving peak flood forecast accuracy with svm model
提高支持向量机洪水峰值预报精度研究 |
| 2. | The example indicates that the model has highly forecasting precision
实例表明，该模型预报精度较高。 |
| 3. | The progress case of theory and application investigation of this method is introduced and improved
由于它充分考虑了系统的时变的特性，因此大大地提高了预报精度。 |
| 4. | Wavelet networks are used to model the prediction error to compensate for the predictive output
为了提高输出预报精度，采用小波网络对预报误差进行预测，作为输出预报的补偿。 |
| 5. | The comparison shows that the method is feasible and the prediction by this method is preciser than the tide table
将预报结果和潮汐表比较，结果表明，此方法可行，预报精度比潮汐表略有提高。 |
| 6. | Compared with the predict consequence and the bp neural network consequence , the prediction accuracy can satisfy the enginneering application
预报结果同有限元结果对比表明，预报精度可满足工程应用。 |
| 7. | Being a black - box model , ann model gets rid of some classical defects of white - box model , longer the system runs , more precise the system will be
该模型克服了白盒子模型的典型缺点，预报精度随着运行时间的增加不断提高。 |
| 8. | Thus , it is one of the key issues in reservoir risk operation to externally analyze and understand the precision of flood and rainfall forecast
为此，客观的分析与认识流域洪水预报和短期降雨预报精度是水库汛限水位动态控制的关键问题之一。 |
| 9. | Examples show that the suggested model can reflect the extreme trend of recorded annual runoff dynamic variation with satisfactory simulation and predicting precision
实例研究表明，灰色自记忆模型能很好地反映动态数据序列的极值趋势，且具有较满意的拟合及预报精度。 |
| 10. | In order to improve the precision of the model , a new method using self adaption and artificial neural networks to predict rolling force was developed
为了提高中厚板轧机轧制力的预报精度，采用轧制力模型自适应与人工神经元网络相结合的方法进行中厚板轧制力的在线预报。 |
