The Cognitive System of Robots Based on Deep Learning with Stable Convergence
學年 113
學期 2
出版（發表）日期 2025-02-20
作品名稱 The Cognitive System of Robots Based on Deep Learning with Stable Convergence
作品名稱（其他語言）
著者 Hsu, Min-jie
單位
出版者
著錄名稱、卷期、頁數 International Journal of Fuzzy Systems 22(1),　p.1-10
摘要 With the advance of deep learning, improving the understanding and cognition of artificial intelligence (AI) systems has become an increasingly crucial research trend. Although most AI studies have focused on improving the efficiency and reach of deep learning technologies for the next wave of nascent AI solutions, they have also highlighted the real-world challenges and limitations of current deep learning approaches. In view of this, this paper proposes a novel cognitive system based on deep learning. To mathematically analyze and solve the critical problem of unstable convergence existing in general cognitive systems, we propose a system framework consisting of three models: a perception model, a hypothesis model, and a memory model. In contrast to conventional reinforcement learning systems, the online learning of our proposed cognitive system can be carried out by only comparing the current outputs with the expected inputs. Then, the memory model (as an evaluation model) can estimate the learning results more accurately so that the hypothesis model is capable of generating improved hypotheses. The contribution of our method is to refer to the memory theory in cognitive psychology to improve the stability of the image-to-robot motor end-to-end learning system. Moreover, an auto-encoder, as the perception model, can encode an observed image into a perception code as the features to easily find an optimal solution. To validate the effectiveness of the proposed cognitive system, Chinese calligraphy writing tasks are used to evaluate its performance. Experimental results show that the proposed cognitive system significantly enhances the online learning process with stable convergence and improves the writing performance of the calligraphy work.
關鍵字 Cognitive models;Perception and psychophysics;Self-modifying machines;Machine learning;Artificial Intelligence
語言 en_US
ISSN 2199-3211
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126792 )