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Autonomous cognition development with lifelong learning: a self-organizing and reflecting cognitive network
Neurocomputing ( IF 6 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.neucom.2020.09.027
Ke Huang , Xin Ma , Rui Song , Xuewen Rong , Yibin Li

Abstract Lifelong learning is still a great challenge for cognitive robots since the continuous streaming data they encounter is usually enormous and no-stationary. Traditional cognitive methods suffer from large storage and computation consumption in this situation. Therefore, we propose a self-organizing and reflecting cognitive network (SORCN) to realize robotic lifelong cognitive development through incremental learning and regular reflecting. The network integrates a self-organizing incremental neural network (SOINN) with a modified CFS clustering algorithm. SOINN develops concise object concepts to alleviate storage consumption. Moreover, we modify SOINN by an efficient competitive method based on reflection results to reduce the learning computation. The modified CFS clustering algorithm is designed for reflecting knowledge learned by SOINN periodically. It improves the traditional CFS as a three-step clustering method including clustering, merging and splitting. Specifically, an autonomous center selection strategy is employed for CFS to cater to online learning. Moreover, a series of cluster merging and splitting strategies are proposed to enable CFS to cluster data incrementally and improve its clustering effect. Additionally, the reflection results are utilized to adjust the topological structure of SOINN and guide the future learning. Experimental results demonstrate that SORCN can achieve better learning effectiveness and efficiency over several state-of-art algorithms.

中文翻译:

终身学习的自主认知发展:自组织和反映认知网络

摘要 终身学习对于认知机器人来说仍然是一个巨大的挑战,因为它们遇到的连续流数据通常是巨大的且非平稳的。在这种情况下,传统的认知方法会遭受大量的存储和计算消耗。因此,我们提出了一种自组织和反思的认知网络(SORCN),通过增量学习和定期反思来实现机器人终身认知发展。该网络将自组织增量神经网络 (SOINN) 与改进的 CFS 聚类算法集成在一起。SOINN 开发了简洁的对象概念来减少存储消耗。此外,我们通过基于反射结果的有效竞争方法修改 SOINN,以减少学习计算。改进的 CFS 聚类算法是为定期反映 SOINN 学习到的知识而设计的。它改进了传统的 CFS 作为三步聚类方法,包括聚类、合并和分裂。具体来说,CFS 采用自主中心选择策略来满足在线学习的需求。此外,还提出了一系列集群合并和分裂策略,使CFS能够增量地对数据进行聚类,提高其聚类效果。此外,反射结果用于调整 SOINN 的拓扑结构并指导未来的学习。实验结果表明,与几种最先进的算法相比,SORCN 可以实现更好的学习效果和效率。合并和分裂。具体来说,CFS 采用自主中心选择策略来满足在线学习的需求。此外,还提出了一系列集群合并和分裂策略,使CFS能够增量地对数据进行聚类,提高其聚类效果。此外,反射结果用于调整 SOINN 的拓扑结构并指导未来的学习。实验结果表明,与几种最先进的算法相比,SORCN 可以实现更好的学习效果和效率。合并和分裂。具体来说,CFS 采用自主中心选择策略来满足在线学习的需求。此外,还提出了一系列集群合并和分裂策略,使CFS能够增量地对数据进行聚类,提高其聚类效果。此外,反射结果用于调整 SOINN 的拓扑结构并指导未来的学习。实验结果表明,与几种最先进的算法相比,SORCN 可以实现更好的学习效果和效率。利用反射结果调整SOINN的拓扑结构,指导未来的学习。实验结果表明,与几种最先进的算法相比,SORCN 可以实现更好的学习效果和效率。利用反射结果调整SOINN的拓扑结构,指导未来的学习。实验结果表明,与几种最先进的算法相比,SORCN 可以实现更好的学习效果和效率。
更新日期:2021-01-01
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