摘要
为解决BP神经网络的实际应用问题,将算法优化技术应用到该项研究中。在分析了多种BP算法的优化算法之后,提出了一种优化的分步赋初值算法,在对各种BP优化算法进行分析的基础上,着重研究了初值和BP算法的关系,并建立了神经网络的线性方程,通过求解方程得到了BP神经网络的最优初始权值。研究结果表明:优化算法不仅降低了BP算法的训练时间,使其由13 s缩短到1 s以内,同时,BP赋初值算法的误差曲线由平缓变为直线下降,解决了传统BP算法易陷入局部极小值的问题。
In order to solve the problems in the applications of the BP neural network, the algorithm optimizing was investigated. After the analysis of many improving algorithms, the step-by-step initialization method was established. The relationship between initial value with the BP algorithm was focused, and linear equation of the BP neural network was established on the bas of the analysis of various BP optimization algorithm. Optimal initial weights of BP neural network were obtained from the equation. The experimental results show that the new algorithm not only reduces the training time from 13 s to less than 1, but also makes the error curve change into a straight line, avoiding the local minimum of BP algorithm.
出处
《机电工程》
CAS
2013年第2期245-248,252,共5页
Journal of Mechanical & Electrical Engineering
关键词
BP神经网络
分步初始化
敏感区
线性方程组
Gause—Jardon消元法
back propagation （BP） neural network
decoupled initialization
sensitive arrears
linear equations
Gause-Jardon method of elimination
