Computational dynamics: Modeling and visualizing trajectory flows in phase space

1993 ◽  
Vol 8 (3-4) ◽  
pp. 285-300 ◽  
Author(s):  
Feng Zhao
Keyword(s):  
Phase Space ◽  
Dynamics Modeling ◽  
Computational Dynamics

Modeling of Robot's Low-Speed Motion Nonlinear Dynamics Based on Phase Space Reconstruction Neural Network

2021 ◽  
Author(s):  
Tie Zhang ◽  
Xiaohong Liang ◽  
Yanbiao Zou
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Phase Space ◽  
Industrial Robot ◽  
Phase Space Reconstruction ◽  
Largest Lyapunov Exponent ◽  
Robot Dynamics ◽  
Dynamics Modeling ◽  
Joint Rotation ◽  
Low Speed

Abstract In order to improve the accuracy of the robot dynamics model, a low-speed motion nonlinear dynamics modeling method of industrial robot based on phase space reconstruction neural network is proposed. It is confirmed by the largest Lyapunov exponent of joint motor torque data in advance that the robot has chaotic characteristics at low-speed motion. Therefore, experimental data and chaos theory is used to analyze low-speed motion nonlinear dynamics, instead of separately considering each factor that may cause the robot's nonlinear dynamics. The phase space reconstruction parameters of each joint are determined by autocorrelation method and false nearest neighbor method. Through data preprocessing and analysis, some joint position derivatives related to the torque data change law are determined. After phase space reconstruction, these derivatives are chosen as the inputs of neural network. Experimental results show that the proposed method can better describe the robot's low-speed motion nonlinear dynamics, and has smaller errors compared with ordinary BP neural network in the case of single joint rotation.

scholarly journals The phase space of meaning model of psychopathology: A computer simulation modelling study

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249320
Author(s):  
Johann Roland Kleinbub ◽  
Alberto Testolin ◽  
Arianna Palmieri ◽  
Sergio Salvatore
Keyword(s):  
Neural Networks ◽  
Phase Space ◽  
Cognitive Processes ◽  
Simulation Modelling ◽  
Simulation Design ◽  
Research Directions ◽  
Computational Dynamics ◽  
The Neural Networks ◽  
Low Performance ◽  
Deep Learning Model

Introduction The hypothesis of a general psychopathology factor that underpins all common forms of mental disorders has been gaining momentum in contemporary clinical research and is known as the p factor hypothesis. Recently, a semiotic, embodied, and psychoanalytic conceptualisation of the p factor has been proposed called the Harmonium Model, which provides a computational account of such a construct. This research tested the core tenet of the Harmonium model, which is the idea that psychopathology can be conceptualised as due to poorly-modulable cognitive processes, and modelled the concept of Phase Space of Meaning (PSM) at the computational level. Method Two studies were performed, both based on a simulation design implementing a deep learning model, simulating a cognitive process: a classification task. The level of performance of the task was considered the simulated equivalent to the normality-psychopathology continuum, the dimensionality of the neural network’s internal computational dynamics being the simulated equivalent of the PSM’s dimensionality. Results The neural networks’ level of performance was shown to be associated with the characteristics of the internal computational dynamics, assumed to be the simulated equivalent of poorly-modulable cognitive processes. Discussion Findings supported the hypothesis. They showed that the neural network’s low performance was a matter of the combination of predicted characteristics of the neural networks’ internal computational dynamics. Implications, limitations, and further research directions are discussed.

A one-dimensional self-gravitating stellar gas

1966 ◽  
Vol 25 ◽  
pp. 46-48 ◽  
Author(s):  
M. Lecar
Keyword(s):  
Gravitational Field ◽  
Phase Space ◽  
Motion Picture ◽  
Space Distribution ◽  
Two Dimensional ◽  
One Dimensional ◽  
Phase Space Distribution ◽  
Dimensional Phase Space ◽  
The Mean

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

PHASE-SPACE CONSIDERATIONS IN THE TRANSVERSE MOMENTUM ANALYSIS

1987 ◽  
Vol 48 (C2) ◽  
pp. C2-233-C2-239
Author(s):  
P. DANIELEWICZ
Keyword(s):  
Phase Space ◽  
Transverse Momentum

Phase space of mechanical systems with a gauge group

1991 ◽  
Vol 161 (2) ◽  
pp. 13-75 ◽  
Author(s):  
Lev V. Prokhorov ◽  
Sergei V. Shabanov
Keyword(s):  
Phase Space ◽  
Gauge Group ◽  
Mechanical Systems

Numerical Study on Particle Distribution in Longitudinal Phase Space and Beam Current Profile using Compact Electron Beam Simulator for Heavy Ion Inertial Fusion

2017 ◽  
Vol 137 (5) ◽  
pp. 344-348
Author(s):  
Takashi Kikuchi ◽  
Yasuo Sakai ◽  
Jun Hasegawa ◽  
Kazuhiko Horioka ◽  
Kazumasa Takahashi ◽  
...  
Keyword(s):  
Electron Beam ◽  
Phase Space ◽  
Particle Distribution ◽  
Numerical Study ◽  
Heavy Ion ◽  
Beam Current ◽  
Inertial Fusion ◽  
Current Profile ◽  
Longitudinal Phase
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