Introduction to Focus Issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics

2020 ◽  
Vol 30 (6) ◽  
pp. 063151 ◽  
Author(s):  
Yang Tang ◽  
Jürgen Kurths ◽  
Wei Lin ◽  
Edward Ott ◽  
Ljupco Kocarev
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
Complex Systems

scholarly journals Toward Reverse Engineering to Economic Analysis: An Overview of Tools and Methodology

2021 ◽  
Author(s):  
Marisa Faggini ◽  
Bruna Bruno ◽  
Anna Parziale
Keyword(s):  
Complex System ◽  
Nonlinear Dynamics ◽  
Economic Analysis ◽  
Complex Systems ◽  
Reverse Engineering ◽  
Underlying Mechanism ◽  
Economic Data ◽  
Agent Based ◽  
Economic Agents ◽  
Advantages And Disadvantages

AbstractFollowing the reverse engineering (RE) approach to analyse an economic complex system is to infer how its underlying mechanism works. The main factors that condition the difficulty of RE are the number of variable components in the system and, most importantly, the interdependence of components on one another and nonlinear dynamics. All those aspects characterize the economic complex systems within which economic agents make their choices. Economic complex systems are adopted in RE science, and they could be used to understand, predict and model the dynamics of the complex systems that enable to define and to control the economic environment. With the RE approach, economic data could be used to peek into the internal workings of the economic complex system, providing information about its underling nonlinear dynamics. The idea of this paper arises from the aim to deepen the comprehension of this approach and to highlight the potential implementation of tools and methodologies based on it to treat economic complex systems. An overview of the literature about the RE is presented, by focusing on the definition and on the state of the art of the research, and then we consider two potential tools that could translate the methodological issues of RE by evidencing advantages and disadvantages for economic analysis: the recurrence analysis and the agent-based model (ABM).

Nonlinear dynamics and machine learning of recurrent spiking neural networks

2021 ◽  
Author(s):  
Oleg V. Maslennikov ◽  
Mechislav M. Pugavko ◽  
Dmitrii S. Shchapin ◽  
Vladimir I. Nekorkin
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
Neural Networks ◽  
Spiking Neural Networks

Diagnostics of the 3D printing process on a CNC machine using machine learning approaches

2021 ◽  
pp. 55-59
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
P.V. Kolchin ◽  
A.V. Kiselev
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
3D Printing ◽  
Electric Arc ◽  
Cyber Physical System ◽  
Learning Approaches ◽  
Cnc Machine ◽  
Printing Process ◽  
Optimization Of Process Parameters ◽  
The Stability

Diagnostics and optimization of the dynamics of an electric arc during 3D printing on a CNC machine are considered. The application of nonlinear dynamics methods in assessing the stability of the 3D printing process and the use of artificial neural networks in the classification and optimization of process parameters are shown. Keywords: 3D printing, cyber physical system, machine learning, hybrid processing, neuroform controller, diagnostics, digital twin. [email protected]

Nonlinear dynamics and machine learning of recurrent spiking neural networks

2021 ◽  
Author(s):  
Oleg V. Maslennikov ◽  
Mechislav M. Pugavko ◽  
Dmitrii S. Shchapin ◽  
Vladimir I. Nekorkin
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
Neural Networks ◽  
Spiking Neural Networks
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