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Psychological Memory Forgetting Model Using Linear Differential Equation Analysis

Author

Listed:
  • JIANFANG GUO

    (School of Marxism, Hohai University, Nanjing, P. R. China†School of Marxism, Nanjing University of Chinese Medicine, Nanjing, P. R. China)

  • AIIAD A. ALBESHRI

    (��Department of Computer Science, Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia)

  • YOLANDA GUERRERO SANCHEZ

    (�Department of Anatomy and Psicobiology, Faculty of Medicine, University of Murcia, Spain)

Abstract

To solve the problem of insufficient combination of neural mechanism and psychological mechanism in the study of human brain cognitive memory activity, in this exploration, the memory forgetting model in psychology is analyzed and constructed by fractional order model of linear differential equation. The study of memory in cognitive psychology is combined with the neurophysiological mechanism of memory. At the same time, for the stability of long-term memory, a long-term memory neural network model (LTMNNS) is proposed. The validity and accuracy of the model are tested by the recognition of memory eigenvectors. Numerical test and simulation results show that when the difference between any initial values of the system is not more than δ, the corresponding system solution difference is not more than 𠜀 in the interval [0,T]. Reasoning correctness of fractional order computation model of memory neural network is verified. The LTMNNS algorithm in this exploration guarantees a high number of completed tasks, and the average number of failed tasks is less than MemNNs, DNC and LSTM algorithm. Compared with other models, the proposed LTMNNS has better generalization ability and higher stability.

Suggested Citation

  • Jianfang Guo & Aiiad A. Albeshri & Yolanda Guerrero Sanchez, 2022. "Psychological Memory Forgetting Model Using Linear Differential Equation Analysis," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(02), pages 1-12, March.
    • Handle: RePEc:wsi:fracta:v:30:y:2022:i:02:n:s0218348x22400801
    DOI: 10.1142/S0218348X22400801
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