A novel memristive electronic synapse-based Hermite chaotic neural
network with application in cryptography
Xinli Shi
a
, Shukai Duan
a,
n
, Lidan Wang
a
, Tingwen Huang
b
, Chuandong Li
a
a
School of Electronic and Information Engineering, Southwest University, Chongqing, China
b
Department of Electrical and Computer Engineering Texas A&M University, Doha, Qatar
article info
Article history:
Received 10 August 2014
Received in revised form
30 October 2014
Accepted 5 March 2015
Communicated by He Huang
Available online 28 April 2015
Keywords:
Hermite neural network
Memristor
Chaotic
Encryption
Decryption
abstract
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.
& 2015 Elsevier B.V. All rights reserved.
1. Introduction
The concept of memristor was initially proposed by Chua in
1 971 [1]. However, it is until the year of 2008 that HP Lab declared
the physical realization of the memristor [2–4]. Since then, wide
attention around the world was put on this newly-found element.
Several years' researches have witnessed the proposals of a
number of new memristor models [5–13]. Because of its unique
switch mechanism, natural memory function, continuous input
and output characteristics and nanoscale size, the memristor has
shown great potential in nonvolatile memory, artificial neural
networks and intelligent information etc. and aroused lots of
studies in these fields [14–22]. For example, Afifi et al. studied
the realization of STDP learning rules based on the pulsing
neuromorphic networks with memristor cross-array [14]. Sangho
et al. demonstrated that memristors can be used to implement
programmable analog circuits, leveraging memristor's fine-
resolution programmable resistance without causing perturba-
tions due to the parasitic components [15]. Duan et al. proposed
memristor-based resistive random access memory (MRRAM) and
verified its effectiveness in storing ASCII characters and gray-scale
images in binary format [16]. Duan et al. also studied chaotic
circuity [17,27–31] and memristor-based cellular nonlinear/neural
network [18]. Wang et al. maked important studies of memristor
model and chaos generation [19]. Among all the memristor's
properties shown before, the uniqueness of nanoscale size has
attracted much attention and interests. Because of that character-
istic, a small voltage through it will cause strong change in electric
field and then lead to nonlinear ionic drift, which brings about the
proposal of nonlinear memristor models and related research.
As is known, the applications of digital information have made
great contribution to the rapid development of modern technology
and network. The process of gaining information becomes simpler
and easier, however, the secrecy of communication calls forth the
researchers' attention gradually. In secret communication, crypto-
graphy is one of the basic methods, which is about the practice
and study of techniques for secure communication in the presence
of third parties, in other words, it is about constructing and
analyzing protocols that will overcome the influence of adver-
saries and are related to various aspects in information security,
such as data confidentiality, data integrity, authentication and
non-repudiation. Meanwhile, chaos, with the special superiority in
secure communication, is a seemly irregular inner random motion
in a deterministic system and a chaotic system has the properties
of complex pseudo-randomness and extreme sensitivity to initial
values, therefore it is reasonable to incorporate the conception of
chaotic system into communication encryption. Some valuable
work has been done [23–26]. In 1990, Carroll firstly built a
synchronous chaotic circuit [23]. Several years later, Milanovic
proposed the synchronization of chaotic neural networks [24].
Contents lists available at ScienceDirect
journal h omepage: www.else vier.com/locate/neu com
Neurocomputing
http://dx.doi.org/10.1016/j.neucom.2015.03.018
0925-2312/& 2015 Elsevier B.V. All rights reserved.
n
Corresponding author.
E-mail address: duansk@swu.edu.cn (S. Duan).
Neurocomputing 166 (2015) 487–495
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