本文深入探讨了传统BP学习算法存在的慢速学习与易陷入局部最优的问题，指出其机械性和基础性导致无法有效利用之前的训练经验进行形式化。提出通过引入高级智能特性，如自适应学习率调整、正则化等手段，优化网络结构设计，从而提高学习效率并减少陷入局部最优的风险。重点强调了改进策略在大规模网络应用中的实际效果。
However, practical applications are difficult to be satisfied because of the problems of slow learning and the likelihood of being trapped into a local minimum especially when the size of the network is large.
翻译：传统的BP学习速度慢，以及容易进入局部最大值。
These problems are due to the fact that the learning of BP neural network is mechanical and elementary. It does not have the advanced intelligent characteristic to generalize the previous training experience.
翻译：主要原因在于机械性以及基础性，不能形式化之前的学习过程。
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