结合信息融合和BP神经网络的决策算法

针对网络输入信息复杂多变,固定的 BP(Back-Propagation)网络结构难以发挥其优势的情况,提出了结合信息融合和BP神经网络的决策算法。即根据输入的变化情况,利用D-S证据理论(Dempster-Shafer,D-S)对BP神经网络的结构进行优选。同时使用粒子群(PSO, Particle Swarm Optimization)算法来确定BP神经网络的初值,以改善其收敛速度慢和容易陷入局部极小值的问题。仿真结果显示,结合信息融合和 BP 神经网络的决策算法和BP神经网络相比,有效提高了BP神经网络训练的时间及预测的准确率,在适应复杂多变的输入信息时具有一定的优势。

Decision-Making Algorithm Combining Information Fusion and BP Neural Network

The fixed BP (Back-Propagation) neural network structure can hardly play to its advantage when the input information become complicated and variable. So the decision- making algorithm is proposed, which combines information fusion with BP neural network. That is, using Dempster-Shafer(D-S) evidence theory to select the structure of BP neural network according to the changing input information. Simultaneously, the initial values are optimized by the Particle Swarm Optimization (PSO) algorithm to improve the problem of BP Neural Network's easily trapping into the local minimum and slow convergence rate. The simulation result shows that through the optimization of combined information fusion with BP neural network, the training time and prediction accuracy are more effective than that only using BP neural network, which has certain advantage of adapting to the complex and varied input information.