Prediction of Optimum 3-Bar Truss Model Parameters with an ANN Model

2020 ◽  
pp. 317-324 ◽  
Author(s):  
Melda Yücel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Model Parameters ◽  
Ann Model ◽  
Truss Model

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

2020 ◽  
pp. 814-832
Author(s):  
Nikolaos E. Karkalos ◽  
Angelos P. Markopoulos ◽  
Michael F. Dossis
Keyword(s):  
Network Architecture ◽  
Inverse Kinematics ◽  
Input Data ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Model Parameters ◽  
Ann Model ◽  
Inverse Kinematics Problem ◽  
Ann Models ◽  
And Training

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

scholarly journals An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

2015 ◽  
Vol 27 (3) ◽  
pp. 217-225 ◽  
Author(s):  
Muhammed Yasin Çodur ◽  
Ahmet Tortum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Traffic Accidents ◽  
Model Parameters ◽  
Mean Square ◽  
Ann Model ◽  
Accident Prediction ◽  
Marquardt Algorithm ◽  
Artificial Neural

This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN) approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012). The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km), annual average daily traffic (AADT), the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage), and traffic accidents that occurred in summer (percentage). In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.

scholarly journals Development of an ANN-Based Lumped Plasticity Model of RC Columns Using Historical Pseudo-Static Cyclic Test Data

2019 ◽  
Vol 9 (20) ◽  
pp. 4263 ◽  
Author(s):  
Zhenliang Liu ◽  
Suchao Li
Keyword(s):  
Test Data ◽  
Cyclic Test ◽  
Critical Parameters ◽  
Model Parameters ◽  
Ann Model ◽  
Rc Columns ◽  
Ultimate Deformation ◽  
Lp Model ◽  
Rc Frame Structures ◽  
Lumped Plasticity

This study explores the possibility of using an ANN-based model for the rapid numerical simulation and seismic performance prediction of reinforced concrete (RC) columns. The artificial neural network (ANN) method is implemented to model the relationship between the input features of RC columns and the critical parameters of the commonly used lumped plasticity (LP) model: The strength and the yielding, capping and ultimate deformation capacity. Cyclic test data of 1163 column specimens obtained from the PEER and NEEShub database and other sources are collected and divided into the training set, test set and validation set for the ANN model. The effectiveness of the proposed ANN model is validated by comparing it with existing explicit formulas and experimental results. Results indicated that the developed model can effectively predict the strength and deformation capacities of RC columns. Furthermore, the response of two RC frame structures under static force and strong ground motion were simulated by the ANN-based, bi-linear and tri-linear LP model method. The good agreement between the proposed model and test results validated that the ANN-based method can provide sufficiently accurate model parameters for modeling the seismic response of RC columns using the LP model.

Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

2017 ◽  
Vol 7 (3) ◽  
pp. 20-40 ◽  
Author(s):  
Nikolaos E. Karkalos ◽  
Angelos P. Markopoulos ◽  
Michael F. Dossis
Keyword(s):  
Network Architecture ◽  
Inverse Kinematics ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Model Parameters ◽  
Ann Model ◽  
Inverse Kinematics Problem ◽  
Ann Models ◽  
And Training ◽  
Computing Models

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

scholarly journals Hydrologic forecasting using artificial neural networks: a Bayesian sequential Monte Carlo approach

2010 ◽  
Vol 13 (1) ◽  
pp. 25-35 ◽  
Author(s):  
Kuo-Lin Hsu
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Sequential Monte Carlo ◽  
Model Parameters ◽  
Ann Model ◽  
Streamflow Prediction ◽  
Streamflow Estimation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Non Gaussian

Sequential Monte Carlo (SMC) methods are known to be very effective for the state and parameter estimation of nonlinear and non-Gaussian systems. In this study, SMC is applied to the parameter estimation of an artificial neural network (ANN) model for streamflow prediction of a watershed. Through SMC simulation, the probability distribution of model parameters and streamflow estimation is calculated. The results also showed the SMC approach is capable of providing reliable streamflow prediction under limited available observations.

Very Short-Term Wind Speed Prediction of a Wind Farm Based on Artificial Neural Network

2012 ◽  
Vol 608-609 ◽  
pp. 677-682 ◽  
Author(s):  
Rui Ma ◽  
Shu Ju Hu ◽  
Hong Hua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Power Plants ◽  
Wind Farm ◽  
Model Parameters ◽  
Ann Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Artificial Neural

Wind speed prediction is critical for wind energy conversion system since it not only can relieve or avoid the disadvantageous impact on power system, but also can enhance the competitive ability of wind power plants against others in electricity markets. The model presented in this paper was based on artificial neural network (ANN) and the selection of the model parameters was presented in detail. The autocorrelation function (ACF) of wind speed time series was used to determine the input variables of the neural network. The simulation was carried out with the proposed ANN model.The conclusion that the optimal network structure may be different corresponding to different error evaluation was drawn through a large number of simulation experiments. And the simulaiton results showed that the ANN model is less than 10.77% in terms of root mean square error and 5.86% in terms of mean absolute error compared with the persistence model.

Performance prediction of bended radio-frequency capacitors and inductors on plastic substrates using artificial neural network

2021 ◽  
pp. 2150288
Author(s):  
Kuibo Lan ◽  
Fei Wang ◽  
Qijun Zhang ◽  
Zhenqiang Ma ◽  
Guoxuan Qin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radio Frequency ◽  
Flexible Electronics ◽  
High Performance ◽  
Original Method ◽  
Model Parameters ◽  
Ann Model ◽  
Plastic Substrates ◽  
Artificial Neural

Flexible radio-frequency (RF) capacitors and inductors on the plastic substrates have been fabricated and characterized under mechanical bending conditions. A novel method to predict the RF performance for them on different bending states is demonstrated. Artificial neural network (ANN) shows good modeling accuracy for the flexible RF passive components with bending strains from dc to resonant frequency ([Formula: see text] GHz for the capacitor/inductor). More importantly, the automatically generated ANN model, with no need of repeatedly tuning the model parameters, has demonstrated the ability to predict the RF responses for the flexible capacitors and inductors under arbitrary bending conditions with only a few sets of experimental data. Once established, this model can automatically learn the structure of the input date and predict the actual results on specific bending state which can provide an original method to measure the performance for flexible electronics on even extreme bent radius. The ANN model indicates good potential for accurate design, characterization and optimization of the high-performance flexible electronics.

scholarly journals Proposing Several Model Techniques Including ANN and M5P-tree to Forecast the Stress at the Failure of Geopolymer Concrete Mixtures Incorporated Nano-silica

2022 ◽  
Author(s):  
Hemn Unis Ahmed ◽  
Ahmed S. Mohammed ◽  
Azad A. Mohammed
Keyword(s):  
Portland Cement ◽  
Alkaline Solution ◽  
Cementitious Materials ◽  
The Other ◽  
Curing Temperature ◽  
Model Parameters ◽  
Geopolymer Concrete ◽  
Ann Model ◽  
Nano Silica ◽  
Binder Ratio

Abstract Geopolymers are innovative cementitious materials that can completely replace traditional Portland cement composites and have a lower carbon footprint than Portland cement. Recent efforts have been made to incorporate various nanomaterials, most notably nano-silica (nS), into geopolymer concrete (GPC) to improve the composite's properties and performance. Compression strength (CS) is one of the essential properties of all types of concrete composites, including geopolymer concrete. As a result, creating a credible model for forecasting concrete CS is critical for saving time, energy, and money, as well as providing guidance for scheduling the construction process and removing formworks. This paper presents a large amount of mixed design data correlated to mechanical strength using empirical correlations and neural networks. Several models, including artificial neural network, M5P-tree, linear regression, nonlinear regression, and multilogistic regression models were utilized to create models for forecasting the CS of GPC incorporated nS. In this case, about 207 tested CS values were collected from literature studies and then analyzed to promote the models. For the first time, eleven effective variables were employed as input model parameters during the modeling process, including the alkaline solution to binder ratio, binder content, fine and coarse aggregate content, NaOH and Na2SiO3 content, Na2SiO3/NaOH ratio, molarity, nS content, curing temperatures, and ages. The developed models were assessed using different statistical tools such as RMSE, MAE, SI, OBJ value, and R2. Results revealed that the ANN model estimated the CS of GPC incorporated nS more accurately than the other models. On the other hand, the alkaline solution to binder ratio, molarity, NaOH content, curing temperature, and ages were those parameters that have significant influences on the CS of GPC incorporated nS.

scholarly journals Prediction of Water Quality Variation Affected by Tributary Inputs in large Rivers Using ANN Model

10.29007/tvb3 ◽  
2018 ◽  
Author(s):  
Il Won Seo ◽  
Se Hun Yun
Keyword(s):  
Water Quality ◽  
Input Data ◽  
Model Simulation ◽  
Large River ◽  
Quality Data ◽  
Model Parameters ◽  
Ann Model ◽  
Water Quality Data ◽  
Water Quality Variation ◽  
Quality Variation

In this study, an enhanced ANN model was developed to analyze the water quality variation at the river confluence by incorporating the resilient propagation algorithm to increase the model accuracy. An ensemble modeling with stratified sampling method was also developed in order to reduce the influence of the input data and model parameters on the prediction of river water quality. The water quality parameters such as pH, electric conductivity (EC), DO and chlorophyll-a, were predicted using proposed ANN model in the large river which is affected by pollutant inputs from the tributary river. The results of model simulation showed that the pollutant input from the tributary affected the water quality of the mainstream. The model prediction using water quality data of the tributary river as the input data in addition to the mainstream data produced better results than the simulation using mainstream data only, especially for EC and DO, R2 value was improved by 30.9% and 20.6%, respectively.

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