Mathematical Modelling of Mechanical Complex Systems, Volume 1, Discrete Models

1995 ◽  
Vol 79 (484) ◽  
pp. 246
Author(s):  
Ll. G. Chambers ◽  
K. Arczewski ◽  
J. Pietrucha
Keyword(s):  
Complex Systems ◽  
Mathematical Modelling ◽  
Discrete Models

scholarly journals A Mathematical Model of the Effect of Natural Selection on Adaptation Forms that Implemented by disruptive coloration of Taurotragus oryx

2018 ◽  
Author(s):  
Yu. G. Bespalov ◽  
K. V. Nosov ◽  
P. S. Kabalyants
Keyword(s):  
Natural Selection ◽  
Mathematical Modelling ◽  
Discrete Models ◽  
New Class ◽  
Protective Coloration ◽  
Study Results ◽  
The Subject ◽  
Formalized Description ◽  
Adaptive Role

In the study, results of mathematical modelling of the influence of natural selection on performance of various forms of animal adaptation to habitat conditions are presented. For a formalized description of the subject of study, we used a new class of mathematical models— discrete models of dynamical systems. Sets of strategies of protective coloration of antelopes Taurotragus oryx are the subject of a formalized description. Various combinations of brightness of green and red components of gray-brown non-uniform protective coloration of different parts of the silhouette of these animals were considered as such strategies. The sets based on the material of digital pictures of the two groups of Taurotragus oryx were compared. The first group includes antelopes Taurotragus oryx from Serengeti National Park (Tanzania) exposed to natural selection. The second group includes Taurotragus oryx, actually domesticated in Askania-Nova reserve (Ukraine), for which natural selection is not active. The sets of above mention strategies-combinations, modelled for the two groups, were compared by the numbers of unique combinations of values of brightness of red and green colours, as well as combinations with closest values of these brightness. The adaptive role of combinations with different values of red and green colours was identified with the role of idioadaptations. The adaptive role of combinations with equal values of red and green colours was identified with a more wide performance of aromorphoses. In this connection, the notions “quasi-idioadaptation” and “quasi-aromorphosis” were introduced in the paper.It is assumed that both quasi-aromorphoses and quasi-idiadaptations, in certain conditions, contribute to the destruction of an integral visual perception of the silhouette of an animal against a many-coloured background of vegetation. At that, assumed that an adaptation function of a quasi-aromorphosis can be implemented in a wider range of colorimetric parameters of a plant background. The results of modelling indicate that the coloration of Taurotragus oryx from Serengeti is characterized by a larger set of quasi-adaptations than coloration of Taurotragus oryx from Askania-Nova. In the coloration of the latter, there is no quasi-aromorphosis with maximum values of brightness of both red and green components. But there exists a quasi-aromorphosis in the coloration of Taurotragus oryx from Serengeti. Such results of mathematical modelling correspond to prevailing ideas about the influence of natural selection on the character of adaptive reactions of living beings.

scholarly journals Modelling Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway via Artificial Neural Networks

Computation ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 64
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak ◽  
Junlin Hao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Complex Systems ◽  
Mathematical Modelling ◽  
Cellular Automaton ◽  
Computer Simulations ◽  
Autonomous Agents ◽  
Functional Relationships ◽  
Artificial Neural

A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It often involves the use of both traditional and modern statistical approaches, including multiple linear regression, generalized linear model and non-linear regression models such as artificial neural networks. In this work, we first conduct a simulation study of the agents’ decisions learning to cross a cellular automaton based highway and then, we model the simulation data using artificial neural networks. Our research shows that artificial neural networks are capable of capturing the functional relationships between input and output variables of our simulation experiments, and they outperform the classical modelling approaches. The variable importance measure techniques can consistently identify the most dominant factors that affect the response variables, which help us to better understand how the decision-making by the autonomous agents is affected by the input factors. The significance of this work is in extending the investigations of complex systems from mathematical modelling and computer simulations to the analysis and modelling of the data obtained from the simulations using advanced statistical models.

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