A universal variable extension method for designing multi-scroll/wing chaotic systems

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIE.2023.3299020Developing a universal design method to construct different multiscroll/wing chaotic systems (MS/WCSs) has been challenging. This article proposes a general design method for MS $/$ WCSs called the universal variable extension method (UVEM). It is a simple but effective approach that generates one-direction (1-D) and 2-D multiscroll/wing chaotic attractors. Using any double-scroll/wing chaotic system as the basic system, the UVEM is able to construct different MS/WCSs. Employing Chua's chaotic system and Lorenz chaotic system as two examples, we construct two MSCSs (including 1-D and 2-D) and two MWCSs (including 1-D and 2-D), respectively. Theoretical analysis and numerical simulation show that the constructed MS/WCSs not only can generate 1-D and 2-D multiscroll/wing chaotic attractors but also have 1-D and 2-D initial boosting behaviors. This means that the MS/WCSs designed by the UVEM are very sensitive to their initial states, and have better unpredictability and more complex chaotic behaviors. To show the simplicity of UVEM in hardware implementation, we develop a field-programmable gate array-based digital hardware platform to implement the designed MS $/$ WCSs. Finally, a new pseudorandom number generator is proposed to investigate the application of the MS/WCSs. All P-values obtained by the NIST SP800-22 test are larger than 0.01, which indicates that the MS/WCSs designed by UVEM have high randomness.Peer reviewe

Generating n-Scroll Chaotic Attractors From A Memristor-based Magnetized Hopfield Neural Network

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCSII.2022.3212394This brief presents a novel method to generate n-scroll chaotic attractors. First, a magnetized Hopfield neural network (HNN) with three neurons is modeled by introducing an improved multi-piecewise memristor to describe the effect of electromagnetic induction. Theoretical analysis and numerical simulation show that the memristor-based magnetized HNN can generate multi-scroll chaotic attractors with arbitrary number of scrolls. The number of scrolls can be easily changed by adjusting the memristor control parameters. Besides, complex initial offset boosting behavior is revealed from the magnetized HNN. Finally, a magnetized HNN circuit is designed and various typical attractors are verified.Peer reviewe

A multi-stable memristor and its application in a neural network

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Nowadays, there is a lot of study on memristorbased systems with multistability. However, there is no study on memristor with multistability. This brief constructs a mathematical memristor model with multistability. The origin of the multi-stable dynamics is revealed using standard nonlinear theory as well as circuit and system theory. Moreover, the multi-stable memristor is applied to simulate a synaptic connection in a Hopfield neural network. The memristive neural network successfully generates infinitely many coexisting chaotic attractors unobserved in previous Hopfield-type neural networks. The results are also confirmed in analog circuits based on commercially available electronic elements.Peer reviewe

Firing multistability in a locally active memristive neuron model

Funding Information: This work is supported by The Major Research Project of the National Natural Science Foundation of China (91964108), The National Natural Science Foundation of China (61971185), The Open Fund Project of Key Laboratory in Hunan Universities (18K010). Publisher Copyright: © 2020, Springer Nature B.V. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.The theoretical, numerical and experimental demonstrations of firing dynamics in isolated neuron are of great significance for the understanding of neural function in human brain. In this paper, a new type of locally active and non-volatile memristor with three stable pinched hysteresis loops is presented. Then, a novel locally active memristive neuron model is established by using the locally active memristor as a connecting autapse, and both firing patterns and multistability in this neuronal system are investigated. We have confirmed that, on the one hand, the constructed neuron can generate multiple firing patterns like periodic bursting, periodic spiking, chaotic bursting, chaotic spiking, stochastic bursting, transient chaotic bursting and transient stochastic bursting. On the other hand, the phenomenon of firing multistability with coexisting four kinds of firing patterns can be observed via changing its initial states. It is worth noting that the proposed neuron exhibits such firing multistability previously unobserved in single neuron model. Finally, an electric neuron is designed and implemented, which is extremely useful for the practical scientific and engineering applications. The results captured from neuron hardware experiments match well with the theoretical and numerical simulation results.Peer reviewedFinal Accepted Versio

Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control

Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61971185, the Major Research Project of the National Natural Science Foundation of China under Grant 91964108 and the Open Fund Project of Key Laboratory in Hunan Universities under Grant 18K010. Publisher Copyright: © 2020 Elsevier B.V.This paper investigates the synchronization problem of inertial memristive neural networks (IMNNs) with time-varying delays via event-triggered control (ETC) scheme and state feedback controller for the first time. First, two types of state feedback controllers are designed; the first type of controller is added to the transformational first-order system, and the second type of controller is added to the original second-order system. Next, based on each feedback controller, static event-triggered control (SETC) condition and dynamic event-triggered control (DETC) condition are presented to significantly reduce the update times of controller and decrease the computing cost. Then, some sufficient conditions are given such that synchronization of IMNNs with time-varying delays can be achieved under ETC schemes. Finally, a numerical simulation and some data analyses are given to verify the validity of the proposed results.Peer reviewe

A Triple-Memristor Hopfield Neural Network With Space Multi-Structure Attractors And Space Initial-Offset Behaviors

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCAD.2023.3287760Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This paper proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multi-structure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multi-structure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multi-structure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudo-random number generator to explore the potential application of the proposed TM-HNN.Peer reviewe

Xylitol production from xylose mother liquor: a novel strategy that combines the use of recombinant Bacillus subtilis and Candida maltosa

<p>Abstract</p> <p>Background</p> <p>Xylose mother liquor has high concentrations of xylose (35%-40%) as well as other sugars such as L-arabinose (10%-15%), galactose (8%-10%), glucose (8%-10%), and other minor sugars. Due to the complexity of this mother liquor, further isolation of xylose by simple method is not possible. In China, more than 50,000 metric tons of xylose mother liquor was produced in 2009, and the management of sugars like xylose that present in the low-cost liquor is a problem.</p> <p>Results</p> <p>We designed a novel strategy in which <it>Bacillus subtilis </it>and <it>Candida maltosa </it>were combined and used to convert xylose in this mother liquor to xylitol, a product of higher value. First, the xylose mother liquor was detoxified with the yeast <it>C. maltosa </it>to remove furfural and 5-hydromethylfurfural (HMF), which are inhibitors of <it>B. subtilis </it>growth. The glucose present in the mother liquor was also depleted by this yeast, which was an added advantage because glucose causes carbon catabolite repression in <it>B. subtilis</it>. This detoxification treatment resulted in an inhibitor-free mother liquor, and the <it>C. maltosa </it>cells could be reused as biocatalysts at a later stage to reduce xylose to xylitol. In the second step, a recombinant <it>B. subtilis </it>strain with a disrupted xylose isomerase gene was constructed. The detoxified xylose mother liquor was used as the medium for recombinant <it>B. subtilis </it>cultivation, and this led to L-arabinose depletion and xylose enrichment of the medium. In the third step, the xylose was further reduced to xylitol by <it>C. maltosa </it>cells, and crystallized xylitol was obtained from this yeast transformation medium. <it>C. maltosa </it>transformation of the xylose-enriched medium resulted in xylitol with 4.25 g L^-1·h^-1volumetric productivity and 0.85 g xylitol/g xylose specific productivity.</p> <p>Conclusion</p> <p>In this study, we developed a biological method for the purification of xylose from xylose mother liquor and subsequent preparation of xylitol by <it>C. maltosa</it>-mediated biohydrogenation of xylose.</p