首页>
外文OA文献
>A new fast learning algorithm with promising global convergence capability for feed-forward neural networks
【2h】
A new fast learning algorithm with promising global convergence capability for feed-forward neural networks
展开▼
机译:具有前馈全局收敛能力的前馈神经网络快速学习新算法
展开▼
免费
页面导航
摘要
著录项
相似文献
相关主题
摘要
Backpropagation (BP) learning algorithm is the most widely used supervised learning technique that is extensively applied in the training of multi-layer feed-forward neural networks. Although many modifications of BP have been proposed to speed up the learning of the original BP, they seldom address the local minimum and the flat-spot problem. This paper proposes a new algorithm called Local-minimum and Flat-spot Problem Solver (LFPS) to solve these two problems. It uses a systematic approach to check whether a learning process is trapped by a local minimum or a flat-spot area, and then escape from it. Thus, a learning process using LFPS can keep finding an appropriate way to converge to the global minimum. The performance investigation shows that the proposed algorithm always converges in different learning problems (applications) whereas other popular fast learning algorithms sometimes give very poor global convergence capabilities.
展开▼