一种改进的BP神经网络算法及其应用

BP算法是目前应用最为广泛的神经网络学习算法,但原始算法收敛速率慢,训练过程易陷入局部极小值以及隐层节点数选择困难.针对这些问题提出了不少改进措施,文中提出了一种改进的BP神经网络算法,在BP算法基础上,从训练算法着手,通过误差的变化趋势,动态调整权值以提高网络的收敛速率;通过数学推导,从理论上验证了该算法的有效性.用MATLAB软件对文中的改进算法进行仿真,并且与其它方法进行比较,结果表明,改进后的算法在收敛速率和抑制噪声等方面有很好的效果,从实验上验证了该算法的有效性.

An Improved Back-propagation NN Algorithm And Its Application

The BP algorithm is at present applies the most widespread neural network study algorithm, but primitive algorithm speed of convergence slow, the training process is easy to fall into the partial minimum as well as the choice of the hidden level pitch point is difficult. In view of these questions many improved measures proposed, this paper proposed a kind of improvement BP neural network algorithm, in the BP algorithm foundation, begins from the training algorithm, through the variation tendency of the error, adjust right value by enhancing the network speed of convergence dynamically;through mathematical reasoning, has theoretically verified the validity of the algorithm. Carries on the simulation with the MATLAB software to this improvement algorithm, and carries on the comparison with the other methods, finally indicated that the improved algorithm has the very good effect in the aspects of the speed of convergence and noise control, has experimentally verified the validity of the algorithm.