This study aims to design a neural model for a linear or nonlinear systems by using an Evolutionary Programming algorithm (EP) to choose the optimal structural construction for the network. We have used Matlab to design Neural Networks using (EP), be
cause of its flexibility and ability to represent matrices (Cell Arrays, Multi Dimension Arrays). The experimental results confirm the efficiency with which this algorithm (EP) obtains the optimal network. We have tested the algorithm performance and the resulting model robustness by canceling one of the hidden layer nodes of the best net resulting from applying (EP). The effectiveness of that canceling on the resulting model output is also tested, and this study has shown the efficiency of the algorithm (EP) for the class of systems used.
This paper presents a new technique based on artificial neural networks (ANNs) to
correct power factor. A synchronous motor controlled by the neural controller was used to
handle the problem of reactive power compensation of the system, in order to
correct
power factor.
In this paper, the electrical system and the neural controller were simulated using
MATLAB. The results have shown that the presented technique overcomes the problems
in conventional compensators (using static capacitors) such as time delay and step changes
of reactive power besides to the fast compensation compared to the technique with
capacitors groups.
This study constitutes a preliminary step to develop a mathematical model for predicting traffic accidents in the city of Lattakia, based on a number of external factors, which include engineering characteristics, traffic incursions, and traffic acci
dent data. As for its main goal, it is to reduce the number of traffic accidents expected in the future on the main streets in the city, as the study was conducted on various arterial streets in them in terms of their importance and in terms of the number of traffic accidents recorded on them, and in terms of the diversity of their engineering characteristics, in order to have sufficient familiarity with the traffic conditions in The city for various reasons, does not depend on the human behavior of the drivers or on the characteristics of the vehicle.
A statistical analysis of traffic accident data for the years 2014, 2015, 2016 and 2017 was conducted on urban streets in Lattakia, where accidents were classified according to their severity, time of occurrence and place of their occurrence, and the necessary data were collected and digitized within a software environment in Microsoft Excel, and then a model was built Predicting the use of the artificial neural networks tool in the MATLAB program, in which data for 319 traffic accidents that were recorded in the years 2015, 2016 and 2017, were entered, which were divided into three groups (training, validation and testing). The structural neural network (10-10-1) gave high values of the correlation coefficient, as the total R value during the three stages was 0.931236, which is very close to one, and therefore the designed network is ideal and achieves the response to predict traffic accidents monthly with very high accuracy.
