| 基于多模型数据融合算法的木材干燥过程建模 |
| |
| 引用本文： | 刘德胜,张佳薇.基于多模型数据融合算法的木材干燥过程建模[J].传感器与微系统,2007,26(7):82-84. |
| |
| 作者姓名： | 刘德胜 张佳薇 |
| |
| 作者单位： | 1. 东北林业大学,黑龙江,哈尔滨,150040;佳木斯大学,黑龙江,佳木斯,154007
2. 东北林业大学,黑龙江,哈尔滨,150040 |
| |
| 摘 要： | 提出了一种适合木材干燥过程建模的多模型数据融合算法,通过该方法构建了数据融合模型。分别用BP神经网络和动态递归网络建立了木材干燥基准模型,利用自适应加权算法对两模型输出进行融合,通过实验干燥数据仿真表明:融合后的木材含水率预测值的方差为0.125 3,高于任何一个单独模型的预测精度。
|
| 关 键 词： | 数据融合 木材干燥 神经网络 建模 |
| 文章编号： | 1000-9787（2007）07-0082-03 |
| 修稿时间： | 2006-12-18 |
Modeling of wood drying schedule based on multi-model data fusion modeling algorithms |
| |
| LIU De-sheng,ZHANG Jia-wei.Modeling of wood drying schedule based on multi-model data fusion modeling algorithms[J].Transducer and Microsystem Technology,2007,26(7):82-84. |
| |
| Authors: | LIU De-sheng ZHANG Jia-wei |
| |
| Affiliation: | 1. Northeast Forestry University, Harbin 150040, China; 2. Jiamusi University, Jiamusi 154007, China |
| |
| Abstract: | A multi-model data fusion algorithm which fits the modeling of wood drying schedule is proposed,and data fusion model is established based on the algorithms. First, the model of wood drying schedule is constructed by BP neural network and dynamical recurrent neural network, then, a multi-model fusion-modeling algorithm is designed,the models are simulated by using the measured data of the experimental drying kiln, the numerical simulation results show that the fusion-modeling method predicting standard deviation is 0. 125 3, it has better prediction precision than the other models. |
| |
| Keywords: | data fusion wood drying neural network modeling |
| 本文献已被 维普 万方数据 等数据库收录！ |
|
