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.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 268 ◽  
Author(s):  
Ivaldo Tavares Júnior ◽  
Jonas Rocha ◽  
Ângelo Ebling ◽  
Antônio Chaves ◽  
José Zanuncio ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Model Fit ◽  
P Value ◽  
Diameter Class ◽  
The Real ◽  
Total Data ◽  
Artificial Neural ◽  
Clone Group

Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).

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