基于可信度小波神经网络的多传感器数据融合方法

针对传感器数据的多样性, 提出一种基于小波和神经网络数据融合的改进方法. 首先, 对传感器数据进行预处理; 然后, 用小波和BP神经网络相结合的方法优化数据; 最后, 利用计算传感器可信度对数据进行融合. 传感器数据融合效果对比实验结果表明, 该算法针对数据预处理和数据融合的稳定性和有效性均较好, 融合结果的离散程度优于加权数据融合和Kalman数据融合等方法.

Fusion Method Based on Credible Wavelet Neural Network for Multi sensor Data

In view of the diversity of sensor data, we proposed an improved method based on wavelet and neural network data fusion. Firstly,the sensor data was pre processed. Secondly, wavelet and BP neural network were combined to optimize the data. Finally, the data was fused by calculating thecredibility of the sensor. The experimental results of the sensor data fusion effect show that the algorithm is stable and effective for data processing and data fusion. The degree of dispersion of the fusion result is better than that of weighted data fusion, Kalman data fusion and other methods.