Estimation and Application of Artificial Neural Networks’ Weight Based on Kalman Filter Technique

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Estimation and Application of Artificial Neural Networks’ Weight Based on Kalman Filter Technique

更新时间：2023-11-28

- Estimation and Application of Artificial Neural Networks’ Weight Based on Kalman Filter Technique
- Advanced Engineering Sciences Vol. 40, Issue 4, Pages: 25-28(2008)
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覃光华,王顺久,缪韧.基于卡尔曼滤波技术的人工神经网络权重估算及应用[J].工程科学与技术,2008,40(4):25-28.

Estimation and Application of Artificial Neural Networks’ Weight Based on Kalman Filter Technique. [J]. Advanced Engineering Sciences 40(4):25-28(2008)
覃光华,王顺久,缪韧.基于卡尔曼滤波技术的人工神经网络权重估算及应用[J].工程科学与技术,2008,40(4):25-28. DOI：

Estimation and Application of Artificial Neural Networks’ Weight Based on Kalman Filter Technique. [J]. Advanced Engineering Sciences 40(4):25-28(2008) DOI：

摘要

中文摘要: 为改进神经网络模型算法，将神经网络技术与卡尔曼滤波技术进行耦合。在样本训练过程中，将卡尔曼滤波递推算法用于神经网络权重的训练，然后用训练得到的权重进行检验。文中以岷江上游段紫坪埔水文站的流量预报为实例，并与单一的神经网络模型以及卡尔曼滤波模型进行了比较。应用结果表明，卡尔曼技术用于神经网络权重估算，可改善水文预报精度。

Abstract

Abstract:The paper combines artificial neural networks (ANNs) with Kalman filter real time adjustment technique in order to improve traditional ANNs model. The weights are trained by Kalman filter real time adjustment technique in the process of sample training

and then the weights are used for check.One case is flow forecasting for upper reach of Minjing River at Zipingpu station by using the method proposed in the paper

and the results are compared with single ANNs model and single Kalman filter model. The results show if Kalman filter technique is used in estimating networks weights

hydrologic forecast accuracy may be improved.

关键词

神经网络卡尔曼滤波权重

Keywords

neural networksKalman filterweight

references

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