Submit to Entropy Review for Entropy Propose a Special Issue

Journal Browser

Entropy and Stochastic Distribution Optimization for Large-Scale Dynamical Systems

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 10098

Share This Special Issue

Special Issue Editors

Dr. Qichun Zhang

E-Mail Website
Guest Editor

Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: stochastic systems; control systems; non-gaussian systems; entropy optimisation; stochastic distribution control; probabilistic decoupling; performance enhancement; non-gaussian filtering; data-driven design and optimisation; computational neuroscience; brain-computer interface
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chi-Hua Chen

E-Mail Website
Guest Editor

College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
Interests: healthcare systems; augmented reality; big data; deep learning; internet of things; data mining
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jianhua Zhang

E-Mail Website
Guest Editor

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Interests: energy conversion and control; artificial intelligence and machine learning; modelling and control of complex industrial processes; networked control systems; fault diagnosis and fault-tolerant control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

With the development of artificial intelligence and Internet of Things technologies, the engineering system becomes more and more complex with strong nonlinear dynamics. Since the uncertainness and randomness of the complex systems affect the prediction of traditional model-based designs, the optimization problem has been defined using entropy and stochastic distribution. Based on the recent literature, it has been shown that entropy and stochastic distribution can be considered a design tool for large-scale engineering systems in terms of performance enhancement. Therefore, this Special Issue focuses on this topic to investigate fundamental and applicable results in order to attenuate systemic randomness.

Motivated by this fundamental research topic, the potential applications can be extended using entropy and stochastic distribution, such as the industrial process, transportation system, energy system, manufacturing, networked system, etc. Based upon the developed new results, more practical applications will generate impact on knowledge transfer.

This topics of interest of this Special Issue include but are not limited to the following research area:

Machine-learning-based system design, fault diagnosis, tolerant control;
Data-driven filtering and monitoring for stochastic dynamic systems;
Stochastic distribution generation via dynamical data sets;
Entropy optimization and modeling for performance enhancement;
Nonlinear system control with randomness attenuation;
Entropy-based image, signal processing and classification;
Decision-making, management, and planning for stochastic systems;
Mathematical extensions for entropy analysis.

Dr. Qichun Zhang
Prof. Dr. Chi-Hua Chen
Prof. Dr. Jianhua Zhang
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. Entropy is an international peer-reviewed open access monthly 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

entropy dynamic optimization
stochastic distribution optimization
minimum entropy filtering
data-driven entropy modeling and control
randomness attenuation for complex dynamic systems

Related Special Issue

Entropy and Stochastic Distribution Optimization for Large-Scale Dynamical Systems II in Entropy

Published Papers (5 papers)

Download All Papers

Research

14 pages, 929 KiB

Open AccessArticle

Tracking Control for Output Probability Density Function of Stochastic Systems Using FPD Method

by Yi Yang, Yong Zhang and Yuyang Zhou

Entropy 2023, 25(2), 186; https://0-doi-org.brum.beds.ac.uk/10.3390/e25020186 - 17 Jan 2023

Viewed by 803

Abstract

Output probability density function (PDF) tracking control of stochastic systems has always been a challenging problem in both theoretical development and engineering practice. Focused on this challenge, this work proposes a novel stochastic control framework so that the output PDF can track a given time-varying PDF. Firstly, the output PDF is characterised by the weight dynamics following the B-spline model approximation. As a result, the PDF tracking problem is transferred to a state tracking problem for weight dynamics. In addition, the model error of the weight dynamics is described by the multiplicative noises to more effectively establish its stochastic dynamics. Moreover, to better reflect the practical applications in the real world, the given tracking target is set to be time-varying rather than static. Thus, an extended fully probabilistic design (FPD) is developed based on the conventional FPD to handle multiplicative noises and to track the time-varying references in a superior way. Finally, the proposed control framework is verified by a numerical example, and a comparison simulation with the linear–quadratic regulator (LQR) method is also included to illustrate the superiority of our proposed framework. Full article

(This article belongs to the Special Issue Entropy and Stochastic Distribution Optimization for Large-Scale Dynamical Systems)

► Show Figures

Figure 1

18 pages, 3157 KiB

Open AccessArticle

Superheating Control of ORC Systems via Minimum (h,φ)-Entropy Control

by Jianhua Zhang, Jinzhu Pu, Mingming Lin and Qianxiong Ma

Entropy 2022, 24(4), 513; https://0-doi-org.brum.beds.ac.uk/10.3390/e24040513 - 06 Apr 2022

Cited by 2 | Viewed by 1520

Abstract

The Organic Rankine Cycle (ORC) is one kind of appropriate energy recovery techniques for low grade heat sources. Since the mass flow rate and the inlet temperature of heat sources usually experience non-Gaussian fluctuations, a conventional linear quadratic performance criterion cannot characterize the system uncertainties adequately. This paper proposes a new model free control strategy which applies the (h,φ)-entropy criterion to decrease the randomness of controlled ORC systems. In order to calculate the (h,φ)-entropy, the kernel density estimation (KDE) algorithm is used to estimate the probability density function (PDF) of the tracking error. By minimizing the performance criterion mainly consisting of (h,φ)-entropy, a new control algorithm for ORC systems is obtained. The stability of the proposed control system is analyzed. The simulation results show that the ORC system under the proposed control method has smaller standard deviation (STD) and mean squared error (MSE), and reveals less randomness than those of the traditional PID control algorithm. Full article

(This article belongs to the Special Issue Entropy and Stochastic Distribution Optimization for Large-Scale Dynamical Systems)

► Show Figures

Figure 1

13 pages, 2706 KiB

Open AccessArticle

Molecular Weight Distribution Control for Polymerization Processes Based on the Moment-Generating Function

by Jianhua Zhang, Jinzhu Pu and Mifeng Ren

Entropy 2022, 24(4), 499; https://0-doi-org.brum.beds.ac.uk/10.3390/e24040499 - 01 Apr 2022

Cited by 3 | Viewed by 1668

Abstract

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method. Full article

(This article belongs to the Special Issue Entropy and Stochastic Distribution Optimization for Large-Scale Dynamical Systems)

► Show Figures

Figure 1

16 pages, 1158 KiB

Open AccessArticle

Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment

by Huaqing Peng, Heng Li, Yu Zhang, Siyuan Wang, Kai Gu and Mifeng Ren

Entropy 2022, 24(2), 164; https://0-doi-org.brum.beds.ac.uk/10.3390/e24020164 - 21 Jan 2022

Cited by 5 | Viewed by 2427

Abstract

In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method. Full article

(This article belongs to the Special Issue Entropy and Stochastic Distribution Optimization for Large-Scale Dynamical Systems)

► Show Figures

Figure 1

12 pages, 414 KiB

Open AccessArticle

Variance and Entropy Assignment for Continuous-Time Stochastic Nonlinear Systems

by Xiafei Tang, Yuyang Zhou, Yiqun Zou and Qichun Zhang

Entropy 2022, 24(1), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/e24010025 - 24 Dec 2021

Cited by 4 | Viewed by 2185

Abstract

This paper investigates the randomness assignment problem for a class of continuous-time stochastic nonlinear systems, where variance and entropy are employed to describe the investigated systems. In particular, the system model is formulated by a stochastic differential equation. Due to the nonlinearities of the systems, the probability density functions of the system state and system output cannot be characterised as Gaussian even if the system is subjected to Brownian motion. To deal with the non-Gaussian randomness, we present a novel backstepping-based design approach to convert the stochastic nonlinear system to a linear stochastic process, thus the variance and entropy of the system variables can be formulated analytically by the solving Fokker–Planck–Kolmogorov equation. In this way, the design parameter of the backstepping procedure can be then obtained to achieve the variance and entropy assignment. In addition, the stability of the proposed design scheme can be guaranteed and the multi-variate case is also discussed. In order to validate the design approach, the simulation results are provided to show the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Entropy and Stochastic Distribution Optimization for Large-Scale Dynamical Systems)

► Show Figures