一种动态筛选样本的前向神经网络快速学习算法
A Fast Learning Algorithm of Feedforward Neural Networks Based on Screening Samples Dynamically
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摘要: 从理论上讨论了一类隐含层激励函数满足Mercer条件的前向神经网络学习问题，分析了提高网络学习速度的途径，提出了一种动态筛选样本的前向神经网络快速学习算法。它大大提高了网络学习速度，克服了传统的基于梯度下降的网络学习方法存在的诸多弊端。算法还具有动态确定隐含层神经元数的自构性优点。文中通过具体数值试验验证了上述算法的可行性和优越性。
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关键词:
- 前向神经网络;算法;机器学习
Abstract: The learning issue of feedforward neural networks whose activation function of hidden neurons satisfies Mercer condition is discussed in theory. The approach to improving learning speed is investigated. Then a fast learning algorithm of feedforward neural networks based on screening samples dynamically is proposed, which improves learning speed, solves the abuses of those learning algorithm based on gradient decent method and has the self-configuring advantage by determining the number of hidden neuron dynamically. The reliability and advantage of the proposed algorithm are illustrated concretely through test. -
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