Special Issue "Mathematical Methods and Models Applied in Information Technology and Electrical Engineering"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: 20 September 2022 | Viewed by 2613

Special Issue Editors

Prof. Dr. Haitao Li
E-Mail Website
Guest Editor
School of Mathematics and Statistics, Shandong Normal University, Jinan 250014, Shandong, China
Interests: control theory; network; game theory; logical system; matrix theory
Prof. Dr. June Feng
E-Mail Website
Guest Editor
School of Mathematics, Shandong University, Jinan 250100, Shandong, China
Interests: control theory; information science; engineering mathematics; network; logic
Prof. Dr. Ben Niu
E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong, China
Interests: control theory; fuzzy logic; network; nonlinear system
Dr. Shihua Fu
E-Mail Website
Guest Editor
School of Mathematical Science, Liaocheng University, Liaocheng 252000, Shandong, China
Interests: game theory; logical control; network system; matrix theory

Special Issue Information

Dear Colleagues, 

Logic is a fundamental concept of understanding real words. When logical dynamics is considered in hybrid systems, logical systems are encountered. Logical systems are an effective tool to model genetic regulatory networks as well as social networks, which has attracted much attention from various scientists, including several Nobel prize winners. Logical networks include Boolean networks, multi-valued logical networks, and mix-valued logical networks. In a Boolean network, each gene can take two possible values, 1 and 0, and its value (1 or 0) indicates its measured abundance (expressed or unexpressed; high or low). From a graphical perspective, genes in a Boolean network are nodes in this network, and edges describe regulatory relationships between genes. Based on Boolean networks, several important logical models are established, such as networked evolutionary games, hybrid automata, hybrid Petri nets, finite-field networks, feedback shift registers, fuzzy control, and so on. When delay, impulse, switching, and uncertainty are considered in logical systems, various generalized systems are obtained. Recently, there have been many new developments on logical systems based on the classical theory of studying Boolean networks. This includes but is not limited to the analysis of logical systems, design of logical control, applications of logical systems, and other related issues, such as computational complexity and mathematical foundation.

Prof. Dr. Haitao Li
Prof. Dr. June Feng
Prof. Dr. Ben Niu
Dr. Shihua Fu
Guest Editors

Manuscript Submission Information

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Keywords

  • control theory
  • network
  • logical system
  • information science
  • engineering mathematics
  • matrix theory
  • fuzzy logic
  • nonlinear system

Published Papers (5 papers)

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Research

Article
Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market
Mathematics 2022, 10(9), 1456; https://doi.org/10.3390/math10091456 - 26 Apr 2022
Viewed by 592
Abstract
Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by [...] Read more.
Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns. Full article
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Article
Algebraic Representation of Topologies on a Finite Set
Mathematics 2022, 10(7), 1143; https://doi.org/10.3390/math10071143 - 02 Apr 2022
Viewed by 390
Abstract
Since the 1930s, topological counting on finite sets has been an interesting work so as to enumerate the number of corresponding order relations on the sets. Starting from the semi-tensor product (STP), we give the expression of the relationship between subsets of finite [...] Read more.
Since the 1930s, topological counting on finite sets has been an interesting work so as to enumerate the number of corresponding order relations on the sets. Starting from the semi-tensor product (STP), we give the expression of the relationship between subsets of finite sets from the perspective of algebra. Firstly, using the STP of matrices, we present the algebraic representation of the subset and complement of finite sets and corresponding structure matrices. Then, we investigate respectively the relationship between the intersection and union and intersection and minus of structure matrices. Finally, we provide an algorithm to enumerate the numbers of topologies on a finite set based on the above theorems. Full article
Article
A New Hybrid Synchronization PLL Scheme for Interconnecting Renewable Energy Sources to an Abnormal Electric Grid
Mathematics 2022, 10(7), 1101; https://doi.org/10.3390/math10071101 - 29 Mar 2022
Viewed by 372
Abstract
Today, and especially with the growing interest in distributed renewable energy sources (DRESs), modern electric power systems are becoming more and more complex. In order to increase DRES penetration, grid side converter (GSC) control techniques require appropriate synchronization algorithms that are able to [...] Read more.
Today, and especially with the growing interest in distributed renewable energy sources (DRESs), modern electric power systems are becoming more and more complex. In order to increase DRES penetration, grid side converter (GSC) control techniques require appropriate synchronization algorithms that are able to detect the grid voltage status as fast and accurately as possible. The drawbacks of the published synchronization phase-locked loop (PLL) techniques were structured mainly around the slow dynamic responses, the inaccuracy of extracting the fundamental components of the grid voltages when they contain a DC offset, and the worsening of the imbalance rejection ability facing significant frequency changing. This paper proposes a new synchronization PLL technique ensuring efficient and reliable integration of DRESs under normal, abnormal, and harmonically distorted grid conditions. The proposed PLL uses the mixed second- and third-order generalized integrator (MSTOGI) in the prefiltering stage through its adaptability to power quality and numerous grid conditions and its low sensitivity to input DC and inter-harmonics. Moreover, a modified quasi type-1 PLL (MQT1-PLL), which integrates two compensation blocks for phase and amplitude errors, respectively, has been used in the control loop. The discussion of sizing requirements and the effectiveness of the so-called MSTOGI-MQT1-PLL are tested under grid voltage imbalances and distortions and confirmed through simulation results compared to double second-order generalized integrator PLL (DSOGI-PLL), cascaded delayed signal cancellation PLL (CDSC-PLL), and multiple delayed-signal cancellation PLL (MDSC-PLL). Full article
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Article
Comparative Assessment between Five Control Techniques to Optimize the Maximum Power Point Tracking Procedure for PV Systems
Mathematics 2022, 10(7), 1080; https://doi.org/10.3390/math10071080 - 28 Mar 2022
Viewed by 437
Abstract
Solar photovoltaic (PV) energy production is important in reducing global energy crises since it is transportable, scalable, and highly customizable dependent on the needs of the industry or end-user. In addition, compared to other renewable resources, photovoltaic systems can produce electricity without moving [...] Read more.
Solar photovoltaic (PV) energy production is important in reducing global energy crises since it is transportable, scalable, and highly customizable dependent on the needs of the industry or end-user. In addition, compared to other renewable resources, photovoltaic systems can produce electricity without moving parts and have a long lifespan. Nevertheless, solar photovoltaic (PV) systems provide intermittent output electricity with a nonlinear output voltage. Due to this intermittent availability, PV installations are facing significant challenges. As a result, in PV power systems, a Maximum Power Point Tracker (MPPT), a power extraction mechanism, is required to assure maximum power delivery at any given moment. The main objective of this work is to study the MPPT method of extracting the maximum power from photovoltaic modules under different solar irradiation and temperatures. Several MPPT methods have been developed for photovoltaic systems to achieve MPP, depending on weather conditions and applications, ranging from simple to more complex methods. Among these methods, five techniques have been presented and compared that are P&O perturbation and observation method, INC incremental conductance method, the ANN neural network method, the open circuit voltage based neural network method FVCO, and the neural network method at the base of FCC (short circuit current). Full article
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Article
Set Stability and Set Stabilization of Boolean Control Networks Avoiding Undesirable Set
Mathematics 2021, 9(22), 2864; https://doi.org/10.3390/math9222864 - 11 Nov 2021
Cited by 1 | Viewed by 371
Abstract
The traditional set stability of Boolean networks (BNs) refers to whether all the states can converge to a given state subset. Different from the existing results, the set stability investigated in this paper is whether all states in a given initial set can [...] Read more.
The traditional set stability of Boolean networks (BNs) refers to whether all the states can converge to a given state subset. Different from the existing results, the set stability investigated in this paper is whether all states in a given initial set can converge to a given destination set. This paper studies the set stability and set stabilization avoiding undesirable sets of BNs and Boolean control networks (BCNs), respectively. First, by virtue of the semi-tensor product (STP) of matrices, the dynamics of BNs avoiding a given undesirable set are established. Then, the set reachability and set stability of BNs from the initial set to destination set avoiding an undesirable set are investigated, respectively. Furthermore, the set stabilization of BCNs from the initial set to destination set avoiding a given undesirable set are investigated. Finally, a design method for finding the time optimal set stabilizer is proposed, and an example is provided to illustrate the effectiveness of the results. Full article
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