基于WNN的BP算法的多传感器数据融合研究

小波神经网络（WNN）是将小波理论和神经网络理论结合起来的一种神经网络，有较强的函数学习能力和推广能力及广阔的应用前景。采用基于WNN的BP权值平衡算法对多传感器测量的结果进行特征级的数据融合，融合结果提供给决策级判断。该融合算法避免了BP网络收敛速度慢，易产生局部最优解等缺点，提高了学习的速度、精度。仿真结果表明了该方法的有效性。

Research on multi-sensor data fusion based on WNN-BP algorithm

Wavelet neural network(WNN) is a kind of neural networks,which can closely combine wavelet theory with neural network theory.It has many good abilities of function learning and dissemination with a vast range of prospects for application.The BP weight balance algorithm based on WNN is used in multi-sensor data fusion at the feature level,and then the fusion result is judged by the decision-level.This novel fusion algorithm avoids the problem that BP neural network is easy to be trapped into local minima and its convergence speed is slow,and increases the learning speed and precision.The simulation results show its effectiveness of the proposed algorithm.