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摘要翻译：
本文提出了一种改进的基于高斯-牛顿数值优化方法的反向传播算法。反向传播采用最速下降法。利用不同的数据集对该算法进行了测试，并与最速下降反向传播算法进行了比较。在系统中，利用多层神经网络进行优化。在训练期间观察到了该方法的有效性，因为它对用于测试的数据集收敛速度快。还分析了算法步骤计算对内存的要求。
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英文标题：
《An Improved Gauss-Newtons Method based Back-propagation Algorithm for
Fast Convergence》
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作者：
Sudarshan Nandy, Partha Pratim Sarkar and Achintya Das
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Numerical Analysis 数值分析
分类描述：cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
cs.na是Math.na的别名。大致包括ACM学科类G.1的材料。
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英文摘要：
The present work deals with an improved back-propagation algorithm based on Gauss-Newton numerical optimization method for fast convergence. The steepest descent method is used for the back-propagation. The algorithm is tested using various datasets and compared with the steepest descent back-propagation algorithm. In the system, optimization is carried out using multilayer neural network. The efficacy of the proposed method is observed during the training period as it converges quickly for the dataset used in test. The requirement of memory for computing the steps of algorithm is also analyzed.
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PDF链接：
https://arxiv.org/pdf/1206.4329
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