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Mathematics
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Advances of Intelligent Systems
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Advances of Intelligent Systems

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 4909

Special Issue Editors

Prof. Dr. Fangfei Li

E-Mail Website
Guest Editor
School of Mathematics and the Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Interests: cyber–physical systems; intelligent systems; Boolean networks; cyber security; estimation; deep learning; deep reinforcement learning
Dr. Jiapeng Xu

E-Mail
Co-Guest Editor
Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
Interests: event-triggered state estimation; risk-sensitive filtering; distributed filtering; hidden Markov models

Special Issue Information

Dear Colleagues,

Intelligent systems are widely used in power grids, air and road traffic control systems, communication networks and other practical applications. The rapid development of information technology and big data in recent years has brought about opportunities for the modeling, analysis, calculation, control and optimization of intelligent systems. Intelligent systems and computing, a multi-disciplinary subject, integrates simulation and computer modeling, data analysis, control theory, intelligent optimization, network technology and so on.

This Special Issue aims to publish new theories, methods, algorithms and applications pertaining to the analysis, calculation, optimization and control of intelligent systems. Topics of interest include, but are not limited to, the following:

  • Modeling in intelligent systems;
  • Simulation software;
  • Numerical methods for intelligent systems;
  • Optimization methods;
  • Control problems;
  • Markov decision process;
  • Reinforcement learning;
  • Deep learning.

Prof. Dr. Fangfei Li
Dr. Jiapeng Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent systems
  • reinforcement learning
  • deep learning
  • optimization methods

Published Papers (6 papers)

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Research

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17 pages, 2997 KiB  
Article
Improved Financial Predicting Method Based on Time Series Long Short-Term Memory Algorithm
by Kangyi Li and Yang Zhou
Mathematics 2024, 12(7), 1074; https://doi.org/10.3390/math12071074 - 02 Apr 2024
Viewed by 432
Abstract
With developments in global economic integration and the increase in future economic uncertainty, it is imperative to have the ability to predict future capital in relation to financial capital inflow and outflow predictions to ensure capital optimization is within a controllable range within [...] Read more.
With developments in global economic integration and the increase in future economic uncertainty, it is imperative to have the ability to predict future capital in relation to financial capital inflow and outflow predictions to ensure capital optimization is within a controllable range within the current macroeconomic environment and situation. This paper proposes an automated capital prediction strategy for the capital supply chain using time series analysis artificial intelligence methods. Firstly, to analyze the fluctuation and tail risk of the financial characteristics, the paper explores the financial characteristics for measuring the dynamic VaR from the perspectives of volatility, tail, and peak with the Bayesian peaks over threshold (POT) model. Following this, in order to make the modeling more refined, the forecast targets are split before modeling with seasonal Autoregressive Integrated Moving Average (ARIMA) models and Prophet models. Finally, the time series modeling of the wavelet Long Short-Term Memory (LSTM) model is carried out using a two-part analysis method to determine the linear separated wavelet and non-linear embedded wavelet parts to predict strong volatility in financial capital. Taking the user capital flow of the Yu’e Bao platform, the results prove the feasibility and prediction accuracy of the innovative model proposed. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems)
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18 pages, 1039 KiB  
Article
A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data
by Chao Yu, Yang Zhou and Xiaolong Cui
Mathematics 2023, 11(21), 4543; https://doi.org/10.3390/math11214543 - 03 Nov 2023
Viewed by 550
Abstract
With continuous progress in science and technology, a large amount of data are produced in all fields of the world at anytime and anywhere. These data are unmarked and lack marking information, while manual marking is time-consuming and laborious. Herein, this paper introduces [...] Read more.
With continuous progress in science and technology, a large amount of data are produced in all fields of the world at anytime and anywhere. These data are unmarked and lack marking information, while manual marking is time-consuming and laborious. Herein, this paper introduces a distributed semi-supervised labeling framework. This framework addresses the issue of missing data by proposing an attribute-filling method based on subspace learning. Furthermore, this paper presents a distributed semi-supervised learning strategy that trains sub-models (private models) within each sub-system. Finally, this paper develops a distributed graph convolutional neural network fusion technique with enhanced interpretability grounded on the attention mechanism. This paper assigns weights of importance to the edges of each layer in the graph neural network based on sub-models and public data, thereby enabling distributed and interpretable graph convolutional attention. Extensive experimentation using public datasets demonstrates the superiority of the proposed scheme over other state-of-the-art baselines, achieving a reduction in loss of 50% compared to the original approach. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems)
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21 pages, 1072 KiB  
Article
Event-Triggered Time-Varying Formation Tracking Control for Multi-Agent Systems with a Switching-Directed Topology
by Xiaoya Chen and Huiying Chen
Mathematics 2023, 11(20), 4245; https://doi.org/10.3390/math11204245 - 11 Oct 2023
Viewed by 674
Abstract
This study investigates the problem of time-varying formation tracking (TVFT) control involving event-triggered and switching topological mechanisms. Specifically, TVFT is evaluated with a consensus analysis and deduced via the use of linear matrix inequality techniques combined with Lyapunov stability theory. This strategy obtains [...] Read more.
This study investigates the problem of time-varying formation tracking (TVFT) control involving event-triggered and switching topological mechanisms. Specifically, TVFT is evaluated with a consensus analysis and deduced via the use of linear matrix inequality techniques combined with Lyapunov stability theory. This strategy obtains sufficient conditions for system stability and the feedback and coupling gains. In addition, the TVFT compensational signals are presented in two cases to enhance the algorithm’s applicability. Given that ideal multi-agent systems (MASs) should be highly flexible and resilient, we propose a co-design algorithm that strikes a balance between the need for a lower communication frequency and a reduction in the state disagreements of agents. Finally, the effectiveness of the theoretical analysis is demonstrated through 3D figures and comparison tables, from which it can be concluded that the communication frequency of the MAS was clearly reduced on the basis of ensuring consensus performance via applying the algorithm proposed in this paper. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems)
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17 pages, 418 KiB  
Article
Mean Square Exponential Stability of Stochastic Delay Differential Systems with Logic Impulses
by Chunxiang Li, Lijuan Shen, Fangshu Hui, Wen Luo and Zhongliang Wang
Mathematics 2023, 11(7), 1613; https://doi.org/10.3390/math11071613 - 27 Mar 2023
Cited by 1 | Viewed by 845
Abstract
This paper focuses on the mean square exponential stability of stochastic delay differential systems with logic impulses. Firstly, a class of nonlinear stochastic delay differential systems with logic impulses is constructed. Then, the logic impulses are transformed into an equivalent algebraic expression by [...] Read more.
This paper focuses on the mean square exponential stability of stochastic delay differential systems with logic impulses. Firstly, a class of nonlinear stochastic delay differential systems with logic impulses is constructed. Then, the logic impulses are transformed into an equivalent algebraic expression by using the semi-tensor product method. Thirdly, the mean square exponential stability criteria of nonlinear stochastic delay differential systems with logic impulses are given. Finally, two kinds of stochastic delay differential systems with logic impulses and uncertain parameters are discussed, and the coefficient conditions guaranteeing the mean square exponential stability of these systems are obtained. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems)
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20 pages, 790 KiB  
Article
Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays
by Han Pan, Wenbing Zhang and Luyang Yu
Mathematics 2022, 10(20), 3870; https://doi.org/10.3390/math10203870 - 18 Oct 2022
Viewed by 952
Abstract
This paper mainly investigates the exponential stability of switched neural networks (SNNs) with partial state reset and time-varying delays, in which partial state reset means that only a fraction of the states can be reset at each switching instant. Moreover, both stable and [...] Read more.
This paper mainly investigates the exponential stability of switched neural networks (SNNs) with partial state reset and time-varying delays, in which partial state reset means that only a fraction of the states can be reset at each switching instant. Moreover, both stable and unstable subsystems are also taken into account and therefore, switched systems under consideration can take several switched systems as special cases. The comparison principle, the Halanay-like inequality, and the time-dependent switched Lyapunov function approach are used to obtain sufficient conditions to ensure that the considered SNNs with delays and partial state reset are exponentially stable. Numerical examples are provided to demonstrate the reliability of the developed results. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems)
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Review

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23 pages, 397 KiB  
Review
Stability of Differential Systems with Impulsive Effects
by Chunxiang Li, Fangshu Hui and Fangfei Li
Mathematics 2023, 11(20), 4382; https://doi.org/10.3390/math11204382 - 21 Oct 2023
Cited by 1 | Viewed by 792
Abstract
In this paper, a brief survey on the stability of differential systems with impulsive effects is provided. A large number of research results on the stability of differential systems with impulsive effects are considered. These systems include impulsive differential systems, stochastic impulsive differential [...] Read more.
In this paper, a brief survey on the stability of differential systems with impulsive effects is provided. A large number of research results on the stability of differential systems with impulsive effects are considered. These systems include impulsive differential systems, stochastic impulsive differential systems and differential systems with several specific impulses (non-instantaneous impulses, delayed impulses, impulses suffered by logic choice and impulse time windows). The stability issues as well as the applications in neural networks are discussed in detail. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems)
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