Memristor-based affective associative memory neural network circuit with emotional gradual processes

被引:37
|
作者
Liaon, Meiling [1 ]
Wang, Chunhua [1 ]
Sun, Yichuang [2 ]
Lin, Hairong [1 ]
Xu, Cong [1 ]
机构
[1] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Peoples R China
[2] Univ Hertfordshire, Sch Engn & Comp Sci, Hatfield AL10 9AB, AB, England
关键词
Memristive neural network; Circuit simulation; Associative memory; Affective model; Conditioning reflex; ROBOT CONTROL-SYSTEM; MODEL; SYNCHRONIZATION; DESIGN;
D O I
10.1007/s00521-022-07170-z
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the existing affective associative memory neural network circuits, the change of emotions in the affective associative learning and forgetting processes is abrupt and the intensity of emotions is invariable. In fact, the transition from one emotion to another is a gradual process. In this paper, to realize the progressive changes of emotional intensity in the affective associative memory neural network, the gradual learning, gradual forgetting and gradual transferring processes of emotions are proposed and the memristor-based circuit of the affective associative memory neural network is designed. In the designed circuit, the firing frequency of output neurons is closely correlated with the intensity of emotions. The higher the firing frequency of output neurons, the stronger the emotional intensity. Based on the associative memory rule, the dynamical change of the synaptic weights leads to the gradual variation of the frequencies of output neurons. Thus, the function of variable emotional intensity can be realized and the gradual processes can be achieved. The PSPICE simulation results are given to verify that the proposed circuit could realize the affective learning, forgetting and transferring functions with gradual processes.
引用
收藏
页码:13667 / 13682
页数:16
相关论文
共 50 条
  • [21] Memristor-based BAM circuit implementation for image associative memory and filling-in
    Zijia Yang
    Xiaoping Wang
    Neural Computing and Applications, 2021, 33 : 7929 - 7942
  • [22] A Novel Memristor-Based Circuit Implementation of Full-Function Pavlov Associative Memory Accorded With Biological Feature
    Wang, Zilu
    Wang, Xiaoping
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2018, 65 (07) : 2210 - 2220
  • [23] A memristor-based circuit design of pavlov associative memory with secondary conditional reflex and its application
    Du, Sichun
    Deng, Qing
    Hong, Qinghui
    Wang, Chunhua
    NEUROCOMPUTING, 2021, 463 : 341 - 354
  • [24] A memristor-based neural network circuit with synchronous weight adjustment
    Yang, Le
    Zeng, Zhigang
    Shi, Xinming
    NEUROCOMPUTING, 2019, 363 : 114 - 124
  • [25] Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation
    Sun, Junwei
    Gao, Peilong
    Wen, Shiping
    Liu, Peng
    Wang, Yanfeng
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (07): : 11259 - 11270
  • [26] A new emotion model of associative memory neural network based on memristor
    Wang, Leimin
    Zou, Huayu
    NEUROCOMPUTING, 2020, 410 : 83 - 92
  • [27] Memristor-Based Neural Network Circuit of Emotion Congruent Memory With Mental Fatigue and Emotion Inhibition
    Sun, Junwei
    Han, Juntao
    Wang, Yanfeng
    Liu, Peng
    IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2021, 15 (03) : 606 - 616
  • [28] Memristor-based dual mode whole-process Pavlov associative memory circuit
    Wang, Hongting
    Yang, Yan
    Fu, Qiang
    Wang, Dongqing
    INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, 2024, : 708 - 723
  • [29] Design of In-Situ Learning Bidirectional Associative Memory Neural Network Circuit With Memristor Synapse
    Shi, Jichen
    Zeng, Zhigang
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2021, 5 (05): : 743 - 754
  • [30] Memristor-Based Apple Feature Recall Network Circuit Application with Emotional Influence
    Sun, Junwei
    Yang, Jianling
    Wang, Zicheng
    Wang, Yanfeng
    JOURNAL OF NANOELECTRONICS AND OPTOELECTRONICS, 2022, 17 (04) : 688 - 701