A novel memristive electronic synapse-based Hermite chaotic neural network with application in cryptography Academic Article

abstract

• 2015 Elsevier B.V.. The memristor is a kind of non-linear passive two-terminal electrical device, which is widely applied in neural networks currently. In this paper, a new synaptic weight update learning rule of Hermite neural network is proposed by combining Hermite polynomials with memristors to build a memristive Hermite chaotic neural network (MHCNN). The chaotic series is generated by the weights of the neural network and chaotic initial value. And ultimately we can obtain the ciphertext by encrypting the plaintext. The use of memristors results in a very special neural network, which can not only change the polynomial in neural network but also achieve the diversity, and the confidentiality of communication is also improved effectively.

authors

• Huang, Tingwen

published proceedings

• NEUROCOMPUTING

author list (cited authors)

• Shi, X., Duan, S., Wang, L., Huang, T., & Li, C.

citation count

• 31

complete list of authors

• Shi, Xinli||Duan, Shukai||Wang, Lidan||Huang, Tingwen||Li, Chuandong

publication date

• October 2015

publisher

• Elsevier  Publisher

published in

• Neurocomputing  Journal

keywords

• Chaotic
• Decryption
• Encryption
• Hermite Neural Network
• Memristor

Digital Object Identifier (DOI)

• 10.1016/j.neucom.2015.03.018

start page

• 487

end page

• 495

volume

• 166

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.neucom.2015.03.018