基于误差放大的BP算法改进

An Improved Algorithm of Back Propagation based on Enlarging Error

收敛速度慢和容易陷入局部极小是BP算法典型的缺点,也是后来许多改进算法不能克服的问题。介绍了基于误差放大的BP算法原理,分析其对标准BP算法的改进之处,将该算法从单层推广到多层。分析这种算法的数学依据,针对这种算法存在的问题和不足之处——收敛速度慢和不可调节的变化区,提出了一种改进措施:在放大系数中加入元素,从而使其避免了盲目调节。用matlab对实例进行仿真,相对其他的改进方法仿真结果表明该方法具有更好的收敛性和更高的精度。

The slow convergence speed and local minimalism are the typical defects of the backpropagation （BP） algorithm （also some improved algorithms）. We introduced the classic BP learning algorithm based on enlarging error of neural network and it＇s advantages briefly, investigated the mathematic base of the algorithm,and extend it to multi-layer. Then we analysised the existing problems and disadvantages. To avoid the slow convergence speed and blindness accommmodate ,we put forward an improved step ：append element to the enlarging coefficient which simulated in matlabe. The result proved that compare to other algorithms,the improved BP algorithm have faster convergenceand higher precision.