scholarly journals MOVING OBJECTS TRAJECTOTY PREDICTION BASED ON ARTIFICIAL NEURAL NETWORK APPROXIMATOR BY CONSIDERING INSTANTANEOUS REACTION TIME, CASE STUDY: CAR FOLLOWING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 577-582 ◽  
Author(s):  
M. Poor Arab Moghadam ◽  
P. Pahlavani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reaction Time ◽  
Moving Objects ◽  
Simulation Software ◽  
Sigmoid Function ◽  
Car Following ◽  
Proposed Model ◽  
Micro Simulation ◽  
Artificial Neural

Car following models as well-known moving objects trajectory problems have been used for more than half a century in all traffic simulation software for describing driving behaviour in traffic flows. However, previous empirical studies and modeling about car following behavior had some important limitations. One of the main and clear defects of the introduced models was the very large number of parameters that made their calibration very time-consuming and costly. Also, any change in these parameters, even slight ones, severely disrupted the output. In this study, an artificial neural network approximator was used to introduce a trajectory model for vehicle movements. In this regard, the Levenberg-Marquardt back propagation function and the hyperbolic tangent sigmoid function were employed as the training and the transfer functions, respectively. One of the important aspects in identifying driver behavior is the reaction time. This parameter shows the period between the time the driver recognizes a stimulus and the time a suitable response is shown to that stimulus. In this paper, the actual data on car following from the NGSIM project was used to determine the performance of the proposed model. This dataset was used for the purpose of expanding behavioral algorithm in micro simulation. Sixty percent of the data was entered into the designed artificial neural network approximator as the training data, twenty percent as the testing data, and twenty percent as the evaluation data. A statistical and a micro simulation method were employed to show the accuracy of the proposed model. Moreover, the two popular Gipps and Helly models were implemented. Finally, it was shown that the accuracy of the proposed model was much higher - and its computational costs were lower - than those of other models when calibration operations were not performed on these models. Therefore, the proposed model can be used for displaying and predicting trajectories of moving objects being followed.

scholarly journals Using Monte Carlo method to estimate the behavior of neural training between balanced and unbalanced data in classification of patterns

2018 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Paulo Marcelo Tasinaffo ◽  
Gildárcio Sousa Gonçalves ◽  
Adilson Marques da Cunha ◽  
Luiz Alberto Vieira Dias
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Mean Squared Error ◽  
Classification Problem ◽  
Computational Environment ◽  
The Monte Carlo Method ◽  
Proposed Model ◽  
Artificial Neural

This paper proposes to develop a model-based Monte Carlo method for computationally determining the best mean squared error of training for an artificial neural network with feedforward architecture. It is applied for a particular non-linear classification problem of input/output patterns in a computational environment with abundant data. The Monte Carlo method allows computationally checking that balanced data are much better than non-balanced ones for an artificial neural network to learn by means of supervised learning. The major contribution of this investigation is that, the proposed model can be tested by analogy, considering also the fraud detection problem in credit cards, where the amount of training patterns used are high.

scholarly journals Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

2019 ◽  
Vol 9 (1) ◽  
pp. 1733-1739
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reaction Time ◽  
Waste Cooking Oil ◽  
Process Variables ◽  
Cooking Oil ◽  
Acid Methyl ◽  
Artificial Neural ◽  
Homogenous Catalyst ◽  
Catalyst Load

In the present investigation, the transesterification of waste cooking oil (WCO) to biodiesel over homogenous catalyst KOH have been carried out. To optimize the transesterification process variables both response surface method (RSM) and artificial neural network (ANN) mathematical models were applied to study the impact of process variables temperature, catalyst loading, methanol to oil ratio and the reaction time on biodiesel yield. The experiments were planned with a central composite design matrix using 24 factorial designs. A performance validation assessment was conducted between RSM and ANN. ANN models showed a high precision prediction competence in terms of coefficient of determination (R2 = 0.9995), Root Mean Square Error (RMSE = 0.5702), Standard Predicted Deviation (SEP = 0.0133), Absolute Average Deviation (AAD = 0.0115) compared to RSM model. The concentration of catalyst load was identified as the most significant factor for the base catalyzed transesterification. Under optimum conditions, the maximum biodiesel yield of 88.3% was determined by the artificial neural network model at 60 ºC, 1.05 g catalyst load, 7:1 methanol to oil ratio and 90 min transesterification reaction time. The biodiesel was analyzed by GCMS and it showed the presence of hexadecanoic acid, 9- octadecenoic acid, 9, 12, 15-octadecatrienoic acid, eicosenoic acid, methyl 18-methyl-nonadecanoate, docosanoic acid, and tetracosanoic acid as key fatty acid methyl esters.

scholarly journals The conceptual model of neuro-fuzzy regulation of the microclimate in the room

2018 ◽  
Vol 251 ◽  
pp. 03020
Author(s):  
Andrey Karpenko ◽  
Irina Petrova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Thermal Comfort ◽  
Conceptual Model ◽  
Fuzzy Logic Controller ◽  
Comfort Index ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Artificial Neural

The purpose of this study is to develop a model of neuro-fuzzy regulation of the microclimate in the room. The proposed model consists of an artificial neural network serving to form a comfort index PMV, a fuzzy logic controller for regulating temperature and humidity in the room. This approach makes it easy to manage these parameters through an estimate of the PMV index, which indicates the level of thermal comfort in the room.

scholarly journals A model for soil moisture dynamics estimation based on artificial neural network

2019 ◽  
Vol 81 ◽  
pp. 01017
Author(s):  
Wei Wang ◽  
Shuya Wang ◽  
Jianxia Chang ◽  
Dan Bai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Water Diversion ◽  
Soil Moisture Dynamics ◽  
Growth Environment ◽  
Proposed Model ◽  
Solid Foundation ◽  
Artificial Neural ◽  
Moisture Dynamics

Research on soil moisture estimation models can effectively improve the growth environment of crops. In this paper, the author studied the artificial neural network and variation pattern of soil moisture. Then, application of the model for water diversion estimation was explored based on artificial neural network. On this basis, an optimization algorithm was presented to simulate water diversion. Furthermore, a model for remote sensing of soil moisture dynamics was applied to artificial neural network. It has been proven that the research can optimize the application of the proposed model, laying a solid foundation for future study.

Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Ammar Mohammed Ameen ◽  
Jagadeesh Pasupuleti ◽  
Tamer Khatib ◽  
Wilfried Elmenreich ◽  
Hussein A. Kazem
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Measured Data ◽  
Mean Bias Error ◽  
Output Current ◽  
Pv System ◽  
Proposed Model ◽  
Artificial Neural ◽  
Predicted Values

This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system’s output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model’s accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, −0.21 A (−4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN)

2014 ◽  
Vol 902 ◽  
pp. 431-436 ◽  
Author(s):  
A. Shahpanah ◽  
S. Poursafary ◽  
S. Shariatmadari ◽  
A. Gholamkhasi ◽  
S.M. Zahraee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Waiting Time ◽  
Container Terminal ◽  
Hybrid Approach ◽  
Simulation Software ◽  
Optimum Number ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A queuing network model related to arrival, departure and berthing process of ships at port container terminal is presented in this paper. The important datas collected from PTP port container terminal located at Malaysia. Based on the case study the model was built with using Arena 13.5 simulation software. Especially this study proposes a hybrid approach consisting of Genetic algorithm (GA), Artificial Neural Network (ANN) to find the the optimum number of equipments at berthing area of port container terminal. The input data that used in ANN obtained from Arena results. The main goal of this study is reduced waiting time of each ship at port container terminal, and Based on the result the optimum waiting time 50 will be achieved.

scholarly journals Modeling of Sulfite Concentration, Particle Size, and Reaction Time in Lignosulfonate Production from Barley Straw Using Response Surface Methodology and Artificial Neural Network

BioResources ◽  
2016 ◽  
Vol 11 (4) ◽  
Author(s):  
Maria Guadalupe Serna-Diaz ◽  
Ainhoa Arana-Cuenca ◽  
Joselito Medina-Marin ◽  
Juan Carlos Seck-Tuoh-Mora ◽  
Yuridia Mercado-Flores ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Response Surface Methodology ◽  
Reaction Time ◽  
Response Surface ◽  
Barley Straw ◽  
Artificial Neural

scholarly journals Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5805
Author(s):  
Tianfu Ai ◽  
Bin Xu ◽  
Changle Xiang ◽  
Wei Fan ◽  
Yibo Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simulation Software ◽  
Ducted Fan ◽  
The Neural Network ◽  
Aerial Robot ◽  
Artificial Neural ◽  
Comprehensive Modeling

A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.

scholarly journals Using Deep Learning for Visual Navigation of Drone with Respect to 3D Ground Objects

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2140
Author(s):  
Oleg Kupervasser ◽  
Hennadii Kutomanov ◽  
Ori Levi ◽  
Vladislav Pukshansky ◽  
Roman Yavich
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Time Delay ◽  
Moving Objects ◽  
Visual Navigation ◽  
Navigation Problem ◽  
Position And Orientation ◽  
Artificial Neural ◽  
The Difference

In the paper, visual navigation of a drone is considered. The drone navigation problem consists of two parts. The first part is finding the real position and orientation of the drone. The second part is finding the difference between desirable and real position and orientation of the drone and creation of the correspondent control signal for decreasing the difference. For the first part of the drone navigation problem, the paper presents a method for determining the coordinates of the drone camera with respect to known three-dimensional (3D) ground objects using deep learning. The algorithm has two stages. It causes the algorithm to be easy for interpretation by artificial neural network (ANN) and consequently increases its accuracy. At the first stage, we use the first ANN to find coordinates of the object origin projection. At the second stage, we use the second ANN to find the drone camera position and orientation. The algorithm has high accuracy (these errors were found for the validation set of images as differences between positions and orientations, obtained from a pretrained artificial neural network, and known positions and orientations), it is not sensitive to interference associated with changes in lighting, the appearance of external moving objects and the other phenomena where other methods of visual navigation are not effective. For the second part of the drone navigation problem, the paper presents a method for stabilization of drone flight controlled by autopilot with time delay. Indeed, image processing for navigation demands a lot of time and results in a time delay. However, the proposed method allows to get stable control in the presence of this time delay.

Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density

2017 ◽  
Vol 1 (1) ◽  
pp. 43 ◽  
Author(s):  
Anjar Wanto ◽  
Agus Perdana Windarto ◽  
Dedy Hartama ◽  
Iin Parlina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Population Density ◽  
Network Architecture ◽  
Activation Function ◽  
Sigmoid Function ◽  
Backpropagation Algorithm ◽  
Architecture Model ◽  
Artificial Neural

Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.

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