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A novel memristive neural network with hidden attractors and its circuitry implementation

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Abstract

Neural networks have been applied in various fields from signal processing, pattern recognition, associative memory to artificial intelligence. Recently, nanoscale memristor has renewed interest in experimental realization of neural network. A neural network with a memristive synaptic weight is studied in this work. Dynamical properties of the proposed neural network are investigated through phase portraits, Poincaré map, and Lyapunov exponents. Interestingly, the memristive neural network can generate hyperchaotic attractors without the presence of equilibrium points. Moreover, circuital implementation of such memristive neural network is presented to show its feasibility.

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Authors and Affiliations

  1. School of Electronics and Telecommunications, Hanoi University of Science and Technology, 01 Dai Co Viet, Hanoi, Vietnam

    Viet Thanh Pham

  2. Biomedical Engineering Department, Amirkabir University of Technology, Tehran, 15875-4413, Iran

    Sajad Jafari

  3. Research and Development Centre, Vel Tech University, Avadi, Chennai, 600062, Tamil Nadu, India

    Sundarapandian Vaidyanathan

  4. Physics Department, Aristotle University of Thessaloniki, Thessaloniki, GR-54124, Greece

    Christos Volos

  5. Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, China

    Xiong Wang

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  1. Viet Thanh Pham
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  2. Sajad Jafari
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  3. Sundarapandian Vaidyanathan
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  4. Christos Volos
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  5. Xiong Wang
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Correspondence to Sajad Jafari.

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Pham, V.T., Jafari, S., Vaidyanathan, S. et al. A novel memristive neural network with hidden attractors and its circuitry implementation. Sci. China Technol. Sci. 59, 358–363 (2016). https://doi.org/10.1007/s11431-015-5981-2

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