Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms.

中文翻译：

---

一种基于位置感知的时间池学习算法

分层时间记忆是一种模仿人脑结构和信息处理流程的新型人工神经网络模型。层次时间记忆具有很强的适应性和快速的学习能力，成为当前研究的热点。Hierarchical Temporal Memory 通过时间池学习算法获取并保存输入序列的时间特征。然而，目前的算法在学习时间序列数据时存在学习效率低、学习效果差等问题。本文提出了一种基于位置感知的时间池学习算法。设计了基于位置感知的细胞选择规则和基于相邻输入的树突更新规则，以提高算法的学习效率和效果。通过算法原型，使用三个不同的数据集对算法性能进行测试和分析。实验结果验证了该算法能够快速获取输入序列的完整特征。无论序列中是否存在相似的片段，所提出的算法都比现有算法具有更高的预测召回率和准确率。