基于遗传神经网络的多级信息融合模型研究

Research of hierarchical information fusion model based on genetic algorithm with artificial neural network

针对BP神经网络算法收敛速度慢、易于陷入局部极小点的缺点,提出了一种基于遗传算法和神经网络相结合的多级信息融合模式。先用基于实数编码的遗传算法来优化神经网络中的初始权值,再用优化后的神经网络对多传感器信息进行融合分类,最后将该模型应用在故障分类中,并使用Matalab工具进行了仿真实验。将实验结果同单一的神经网络信息融合模型进行了比较分析,结果表明了该模型有效地提高了信息融合的效率和准确度。

Aimed at the problem of slow speed convergence and local minima of BP neural network,the hierarchical information fusion model based on genetic algorithm with artificial neural network is proposed.Firstly a new neural network is optimized by genetic algorithm,then the optimezed neural network is used to fusion information from sensors and the model is stimulated by Matalab 7.0 in fault diagnosis feild.Compared with the traditonal single neural network information fusion model,the results of experiment show that the model can effectively improve the efficiency and accuracy of information fusion.