摘要： 提出的算法是利用凸函数共轭性质中的Young不等式构造优化目标函数,这个优化目标函数对于权值和隐层输出来说为凸函数,不存在局部最小。首先把隐层输出作为变量进行优化更新,然后快速计算出隐层前后的权值。数值实验表明:此算法简单,收敛速度快,泛化能力强,并大大降低了学习误差。

关键词: 前馈神经网络, Young不等式, 凸函数, 快速学习算法

Abstract: This paper presents a new algorithm for feed forward neural networks based on a new optimal target function constructed according to Young inequality in the conjugate of convex function. The optimal target function is a convex function to the weight values between neurons in different layers and outputs of hidden layers. First, outputs of hidden layers can be optimized with convex optimization method, then the weight values are optimized rapidly. The numerical experiments show that the new algorithm is very simple, can accelerate the convergence rate, strength the generalization capability and reduce the error.

中图分类号: 

• TP183