Special Issue "Optimized Machine Learning Algorithms for Modeling Dynamical Systems"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Prof. Dr. Massimiliano Ferrara
E-Mail Website
Guest Editor
1: University Mediterranea of Reggio Calabria, Department of Law, Economics and Human Sciences, Cittadella Universitaria Complesso Torri - Seconda Torre - Via dell’Universita’ 25 - Reggio Calabria (RC) I-89124 Italy
2: ICRIOS - The Invernizzi Centre for Research in Innovation, Organization, Strategy and Entrepreneurship,Bocconi University - Department of Management and Technology, Via Sarfatti, 25, 20136 Milano (MI) Italy, Office Room: Grafton Building - 4th Floor - Room A2-04
Tel. (+39) 0965 1695446/Tel.: (+39) 02 5836 6130
Interests: mathematical economics; game theory; optimization
Dr. Mehdi Salimi
E-Mail Website
Guest Editor
1: Department of Law, Economics and Human Sciences & Decisions Lab, University Mediterranea of Reggio Calabria, Italy
2: Center for Dynamics, Department of Mathematics, Technische Universität Dresden, Germany
Dr. Ali Ahmadian
E-Mail Website
Guest Editor
Institute for Mathematical Research (INSPEM), Universiti Putra Malaysia, Serdang, Selangor, Malaysia
Dr. Bruno Antonio Pansera
E-Mail Website
Guest Editor
University Mediterranea of Reggio Calabria, Department of Law, Economics and Human Sciences, Cittadella Universitaria Complesso Torri - Seconda Torre - Via dell’Universita’ 25 - Reggio Calabria (RC) I-89124 Italy

Special Issue Information

Dear Colleagues,

Let us ponder those machine learning algorithms that predict real dynamical systems.

Mathematical objects used to make models of physical phenomena dependent on time are dynamical systems. These models are used in economic forecasting, medical issues, environmental modelings, etc. There is an overlap between machine learning and dynamical systems. To address this relation, let us assume a framework for dynamical system learning, using the idea of instrumental–variable regression to transform dynamical system learning to a sequence of machine learning problems. This transformation allows applying a strong literature on machine learning to incorporate many types of prior knowledge. Hence, a family of fast and practical learning algorithms for a variety of dynamical system models are employed to forecast the real behavior of such dynamical systems precisely. Further, machine learning folks often use dynamical systems’ taxonomy and reformulate it to some fancy term to make the idea sound sort of new.

The aim of this Special Issue is to attract leading researchers in these areas in order to include new high-quality results on these topics involving their dynamical properties as well as their symmetry characteristics, both from a theoretical and an applied point of view. Please note that all submitted papers must be within the general scope of the Symmetry journal.

Prof. Dr. Massimiliano Ferrara
Dr. Mehdi Salimi
Dr. Ali Ahmadian
Dr. Bruno Antonio Pansera
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 papers will be 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. Symmetry 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 1400 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

  • machine learning
  • supervised algorithms
  • unsupervised algorithms
  • optimization
  • dynamical systems
  • symmetry
  • real-world applications

Published Papers (1 paper)

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Research

Open AccessArticle
A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
Symmetry 2019, 11(11), 1373; https://doi.org/10.3390/sym11111373 - 06 Nov 2019
Abstract
The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. [...] Read more.
The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. In this paper, a mixed integer programming model for observation tasks is established, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed. The configurable probability of the observation task is obtained by constructing a structural symmetric recurrent neural network, and finally, the optimal task planning scheme is obtained. The experimental results are compared with several typical heuristic search algorithms, which have certain advantages, and the validity of the paper is verified. Finally, future application prospects of the method are discussed. Full article
(This article belongs to the Special Issue Optimized Machine Learning Algorithms for Modeling Dynamical Systems)
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