中文摘要：

针对BP神经网络在训练过程中易陷入局部极小值的问题,提出一种新的基于渐消滤波的神经网络学习算法。该算法采用渐消卡尔曼滤波对神经网络的权值进行更新,有效避免了梯度下降算法产生的局部极小问题;与卡尔曼滤波相比,在渐消滤波中充分考虑了最新量测值的影响,能更合理地利用新的有效信息,从而提高了学习算法的收敛速度。学习后的网络不仅具有普通神经网络的自主学习能力,而且具有渐消滤波的最优估计性能。将提出的神经网络算法应用于SINS/BDS组合导航系统进行仿真验证。结果表明,提出的算法在逼近精度方面优于BP算法和卡尔曼滤波算法,可以有效提高神经网络的泛化能力。

英文摘要：

Aiming at the local minimum problem in the training process of BP neural network,this paper proposes a novel neural network learning algorithm based on the fading Kalman filtering. This algorithm avoids the problem of local minimum value because it provides the fading Kalman filtering for weights optimizing in neural network training. Compared with Kalman filtering,the fading Kalman filtering gives full consideration to the effects of the newest measurements and uses the effective information more reasonably. Consequently,the convergence speed of the algorithm is improved. The network after training not only has the autonomous learning ability of general neural network,but also has optimal estimation performance of the fading Kalman filtering. The proposed algorithm is applied to SINS/BDS integrated navigation system. Simulation results and their analysis demonstrate preliminarily that the approximation accuracy of the proposed learning algorithm is better than those of BP algorithm and Kalman filtering algorithm. The proposed algorithm,we believe,can improve the generalization capability of neural network effectively.