CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor曾毅
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword层次时序记忆模型 离散hopfield网络 动作场景分析 脑皮层
Other Abstract

传统人工神经网络(Artifitial Neural NetworksANN),特别是以深度学习(Deep LearningDL)为代表的模式识别方法,需要基于大规模的数据,通过不断迭代训练降低预定义损失函数,以对神经元间的连接权重进行调整,从而完成学习任务。然而,从生物学原理上来说,人类的认知机理并不与此相符,从根本上来说还无法解释人类仅仅需要小样本的输入即可习得新知识的现象。而如何更好地对动作序列进行抽象、获取并表征蕴含于其中的过程性知识也是现今传统人工神经网络并不能很好解决的问题。

本文主要关注类人学习的序列抽象过程,研究类脑智能算法在动作序列分析等相关任务中获取过程性知识的应用,研究的重点在于小样本训练的序列抽象以及基于类脑皮层的模式识别算法。通过调研关于人脑皮质处理信息时的宏观及微观机制,结合受大脑皮层机理启发的层次时序模型(Hierarchical Temporal MemoryHTM)和离散Hopfield网络(Discrete Hopfield Neural Network),并应用于过程性知识表征、动作序列分析及理解等任务中。



Traditional artifitial neural networks (ANN), especially as deep learning (DL) methods, can get good performance on several pattern recognition tasks with large amounts of data available during training. By iterative training, predefined loss function is gradually reduced, and connection weights between neurons get tuned dynamically, so as to complete the learning task. However, from the perspective of biology, human cognitive mechanism is not consistent with that. Fundamentally, it can not explain why humans can acquire new knowledge only through a few input samples. And also it is still hard for traditional neural networks to extract, characterize and abstract process knowledge contained in action sequences.

This paper mainly focuses on the sequential abstraction process of human-like learning, and studies the application of brain-inspired algorithm in process knowledge acquisition tasks such as action sequence analysis. The research focuses on sequential abstraction of small training set and pattern recognition algorithms based on the cerebral cortex. Through the investigation on the information processing mechanism of cerebral cortex via macro and micro scale, we combined two brain-inspired models Hierarchical Temporal Memory (HTM) and Discrete Hopfield Network, which is applied to process knowledge characterization and action scene understanding.

Based on several brain-inspired algorithms, this paper realizes an anti-noise learning model for small-scale abstract pattern sequences, and puts forward a key frame extraction algorithm for joint motion capture data of a real-scene robot. Furthermore, the action sequences after partition can be classified at atomic level in the form of cortical-friendly binary representation. This paper proves current brain-inspired cortical model with the ability of memorization and conceptual learning of pattern sequences through practice. The key frame algorithm can clearly extract key points in certain motion capture trajectory, and can be applied to more high-level process knowledge acquisition tasks such as action scene analysis and natural language description generation.

Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
潘瑞晗. 基于类脑皮层模型的动作序列获取与分析[D]. 北京. 中国科学院大学,2018.
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