论文部分内容阅读
增量式IHMCAP算法采用适合于混合型学习的FTART神经网络，成功解决了符号学习与神经网络学习精度之间的均衡性问题．该算法还具有较强的增量学习能力，在给系统增加新的示例时，不用重新生成已有判定树和神经网络，只需进行一遍增量学习即可调整原结构以提高学习精度，效率高，速度快．
The incremental IHMCAP algorithm uses the FTART neural network suitable for hybrid learning and successfully solves the problem of balance between learning of symbols and learning accuracy of neural networks. The algorithm also has strong ability of incremental learning. When adding new examples to the system, it does not need to regenerate the existing decision trees and neural networks. The incremental learning can be used to adjust the original structure to improve learning accuracy and efficiency High, fast.