基于二参数的BP神经网络算法改进与应用

BP(back propagation)神经网络是一种监督神经网络学习算法,但原始算法收敛速率慢,训练过程易陷入局部极小值,精度不高。提出了一种改进的BP神经网络算法,某种程度上克服了以上缺点。对文中的改进算法用C语言编程,并利用真实数据,对大学生就业能力进行了预测,实验表明改进算法有效,为高校解决大学生就业能力提供了决策支持。

Improvement and application of BP algorithm with two arguments in neural networks

BP（back propagation） neural network is a supervised neural network learning algorithm,but the original algorithm has a slow convergence rate and low accuracy,and the training process of which is easier to fall into local minimum value.In this paper,we impoved the BP neural network algorithm,which partially overcomes the above drawbacks.An improved BP algorithm is completed with the C programming language.We examined the improved BP algorithm with real data about the employment of students.As is shown,the modified algorithm is effective.And it is also a decision support algorithm for the employment ability of college students.