New memory model for humanoid robots - Introduction Of Co-associative memory using mutually coupled chaotic neural networks

Kazuko Itoh, Hiroyasu Miwa, Yuko Nukariya, Massimiliano Zecca, Hideaki Takanobu, Paolo Dario, Atsuo Takanishi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    Personal robots, which are expected to become popular in the future, are required to be active in joint work and community life with humans. Therefore, we have been developing new mechanisms and functions for a humanoid robot that has the ability to express emotions and to communicate with humans in a human-like manner. In 2004, we introduced the "Behavior Model" and "Consciousness Model" to the robot mental model so that the robot generated various kinds of behavior and an object of the robot's behavior became clear. We implemented the mental model in the Emotion Expression Humanoid Robot WE-4RII (Waseda Eye No.4 Refined II). Also, we have been studying a system of multiple harmonic oscillators (neurons) interacting via chaotic force since 2002. Each harmonic oscillator is driven by chaotic force whose bifurcation parameter is modulated by the position of the harmonic oscillator. In this paper, we propose an associative memory model using mutually coupled chaotic neural networks for generating an optimum behavior to a stimulus. We implemented this model in the Emotional Expression Humanoid Robot WE-4RII (Waseda Eye No.4 Refined II).

    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Pages2790-2795
    Number of pages6
    Volume5
    DOIs
    Publication statusPublished - 2005
    EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC
    Duration: 2005 Jul 312005 Aug 4

    Other

    OtherInternational Joint Conference on Neural Networks, IJCNN 2005
    CityMontreal, QC
    Period05/7/3105/8/4

    Fingerprint

    Robots
    Neural networks
    Data storage equipment
    Neurons

    Keywords

    • Associative Memory
    • Humanoid Robot
    • Neural Network

    ASJC Scopus subject areas

    • Software

    Cite this

    Itoh, K., Miwa, H., Nukariya, Y., Zecca, M., Takanobu, H., Dario, P., & Takanishi, A. (2005). New memory model for humanoid robots - Introduction Of Co-associative memory using mutually coupled chaotic neural networks. In Proceedings of the International Joint Conference on Neural Networks (Vol. 5, pp. 2790-2795). [1556367] https://doi.org/10.1109/IJCNN.2005.1556367

    New memory model for humanoid robots - Introduction Of Co-associative memory using mutually coupled chaotic neural networks. / Itoh, Kazuko; Miwa, Hiroyasu; Nukariya, Yuko; Zecca, Massimiliano; Takanobu, Hideaki; Dario, Paolo; Takanishi, Atsuo.

    Proceedings of the International Joint Conference on Neural Networks. Vol. 5 2005. p. 2790-2795 1556367.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Itoh, K, Miwa, H, Nukariya, Y, Zecca, M, Takanobu, H, Dario, P & Takanishi, A 2005, New memory model for humanoid robots - Introduction Of Co-associative memory using mutually coupled chaotic neural networks. in Proceedings of the International Joint Conference on Neural Networks. vol. 5, 1556367, pp. 2790-2795, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, 05/7/31. https://doi.org/10.1109/IJCNN.2005.1556367
    Itoh K, Miwa H, Nukariya Y, Zecca M, Takanobu H, Dario P et al. New memory model for humanoid robots - Introduction Of Co-associative memory using mutually coupled chaotic neural networks. In Proceedings of the International Joint Conference on Neural Networks. Vol. 5. 2005. p. 2790-2795. 1556367 https://doi.org/10.1109/IJCNN.2005.1556367
    Itoh, Kazuko ; Miwa, Hiroyasu ; Nukariya, Yuko ; Zecca, Massimiliano ; Takanobu, Hideaki ; Dario, Paolo ; Takanishi, Atsuo. / New memory model for humanoid robots - Introduction Of Co-associative memory using mutually coupled chaotic neural networks. Proceedings of the International Joint Conference on Neural Networks. Vol. 5 2005. pp. 2790-2795
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