1,557 research outputs found
On the validity of memristor modeling in the neural network literature
An analysis of the literature shows that there are two types of
non-memristive models that have been widely used in the modeling of so-called
"memristive" neural networks. Here, we demonstrate that such models have
nothing in common with the concept of memristive elements: they describe either
non-linear resistors or certain bi-state systems, which all are devices without
memory. Therefore, the results presented in a significant number of
publications are at least questionable, if not completely irrelevant to the
actual field of memristive neural networks
Synthesizing attractors of Hindmarsh-Rose neuronal systems
In this paper a periodic parameter switching scheme is applied to the
Hindmarsh-Rose neuronal system to synthesize certain attractors. Results show
numerically, via computer graphic simulations, that the obtained synthesized
attractor belongs to the class of all admissible attractors for the
Hindmarsh-Rose neuronal system and matches the averaged attractor obtained with
the control parameter replaced with the averaged switched parameter values.
This feature allows us to imagine that living beings are able to maintain vital
behavior while the control parameter switches so that their dynamical behavior
is suitable for the given environment.Comment: published in Nonlinear Dynamic
Constructing multiwing attractors from a robust chaotic system with non-hyperbolic equilibrium points
We investigate a three-dimensional (3D) robust chaotic system which only holds two nonhyperbolic equilibrium points, and finds the complex dynamical behaviour of position modulation beyond amplitude modulation. To extend the application of this chaotic system, we initiate
a novel methodology to construct multiwing chaotic attractors by modifying the position and amplitude parameters. Moreover, the signal amplitude, range and distance of the generated multiwings can be easily adjusted by using the control parameters, which enable us to enhance the potential application in chaotic cryptography and secure communication. The effectiveness of the theoretical analyses is confirmed by numerical simulations. Particularly, the multiwing
attractor is physically realized by using DSP (digital signal processor) chip
Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of
great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In
this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems
with variable and unknown parameters, despite the bounded disturbance and uncertainty with a
known function in two modes. In the first case, it is a main system with some response systems,
and in the second case, it is a circular synchronization. Using theorems it is proved that the two
synchronization methods are equivalent. Our results show that, we are able to obtain the convergence
of synchronization error and parameter estimation error to zero using Lyapunovās method. The
new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are
proposed such that stability of system is guaranteed. To assess the performance of the proposed
synchronization method, various statistical analyzes were carried out on the encrypted medical
images and standard benchmark images. The results show effective performance of the proposed
synchronization technique in the medical images encryption for telemedicine application.MINECO/ FEDER under the RTI2018-098913-B100
CV20-45250 and A-TIC- 080-UGR18 project
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Robust adaptive anti-synchronization control of multiple uncertain chaotic systems of different orders
The precise anti-synchronization control of uncertain chaotic systems has always remained an interesting problem. The anti-synchronization control of multiple different orders uncertain chaotic systems increases the complexity and enhances the security of the information signal in secure communications. Hence, it confines the hacking in digital communication systems. This paper proposes a novel adaptive control technique and studies the double combination anti-synchronization of multiple different orders uncertain chaotic systems. The proposed adaptive feedback control technique consists of three fundamental nonlinear components. Each component accomplishes a different objective; (i) stability of the closed-loop, (ii) smooth and fast convergence behaviour of the anti-synchronization error, and (iii) disturbance rejection. The theoretical analysis in (i) to (iii) uses the Lyapunov stability theory. This paper also provides parameters adaptation laws that stabilize the uncertain parameters to some constants. The paper discusses the simulation results of two representative examples of four different orders uncertain chaotic systems. These examples demonstrate anti-synchronization among hyperchaotic LĆ¼, uncertain chaotic Shimizu Morioka, uncertain second-order nonlinear duffing, and uncertain parametrically excited second-order nonlinear pendulum systems. The computer-based simulation results certify the efficiency and performance of the proposed anti-synchronization control approach and compare them with peer works
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