Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications

2020 ◽  
Vol 26 (3) ◽  
pp. 209-223
Author(s):  
M. Madhiarasan ◽  
M. Tipaldi ◽  
P. Siano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Network Performance ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Research Fields ◽  
Temperature Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals Prediction of Drape Coefficient by Artificial Neural Network

2015 ◽  
Vol 15 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Adel Ghith ◽  
Thouraya Hamdi ◽  
Faten Fayala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

2012 ◽  
Vol 524-527 ◽  
pp. 1331-1334
Author(s):  
Jun Ni ◽  
Zhan Li Ren ◽  
Guo Qing Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Linear Mapping ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Beam Pump ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Dynamometer Card

Beam pump dynamometer card plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in pattern recognition. This paper illuminates ANN realization in identifying fault kinds of dynamometer cards, including a back-propagation algorithm, characteristics of the Dynamometer card and some examples. It is concluded that the buildup of a neural network and the abstract of dynamometer cards are important to successful application.

scholarly journals Prediction of Neutron Yield of IR-IECF Facility in High Voltages Using Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
A. Sadighzadeh ◽  
A. Salehizadeh ◽  
M. Mohammadzadeh ◽  
F. Shama ◽  
S. Setayeshi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discharge Current ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Cathode Voltage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Maximum Voltage

Artificial neural network (ANN) is applied to predict the number of produced neutrons from IR-IECF device in wide discharge current and voltage ranges. Experimentally, discharge current from 20 to 100 mA had been tuned by deuterium gas pressure and cathode voltage had been changed from −20 to −82 kV (maximum voltage of the used supply). The maximum neutron production rate (NPR) of 1.46 × 107 n/s had occurred when the voltage was −82 kV and the discharge current was 48 mA. The back-propagation algorithm is used for training of the proposed multilayer perceptron (MLP) neural network structure. The obtained results show that the proposed ANN model has achieved good agreement with the experimental data. Results show that NPR of 1.855 × 108 n/s can be achieved in voltage and current of 125 kV and 45 mA, respectively. This prediction shows 52% increment in maximum voltage of power supply. Also, the optimum discharge current can increase 1270% NPR.

scholarly journals APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS

2009 ◽  
Vol 12 (4) ◽  
pp. 94-106 ◽  
Author(s):  
Duc Van Le
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Research Trend ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Runoff Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

scholarly journals Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

2020 ◽  
Author(s):  
Somayeh Ezadi ◽  
Tofigh Allahviranloo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

scholarly journals Weekly Flow Prediction of Ergene River Using an Artificial Neural Network Based Solution Approach

10.29007/4sdr ◽  
2018 ◽  
Author(s):  
M. Tamer Ayvaz ◽  
Ulas Tezel ◽  
Elcin Kentel ◽  
Recep Kaya Goktas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Flow Data ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Solution Approach ◽  
Flow Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The objective of this study is to develop an artificial neural network (ANN) based solution approach to predict the weekly flows of Ergene River which is the largest river in Thrace region of Turkey. In the developed approach, precipitation – flow data relationships have been investigated in order to establish the best model structure to predict streamflow at the selected basin. The developed relationships are then evaluated using a feed forward neural network where back propagation algorithm is used to determine the associated network weights. The performance of the developed ANN based solution approach is evaluated by using the weekly precipitation and flow data collected from different monitoring sites in Ergene River basin. The model results are also compared with HEC-HMS model outputs which is calibrated using the same precipitation and flow data. Results indicate that the proposed ANN based solution approach can be effectively used to predict the weekly flows of Ergene River.

scholarly journals Security wire mesh weaving process modelling with artificial neural network

2018 ◽  
Vol 249 ◽  
pp. 02006
Author(s):  
Kridsada Wongwan ◽  
Wimalin Laosiritaworn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Wire Mesh ◽  
Production Yield ◽  
Back Propagation Algorithm ◽  
Defect Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Inspection Data

This paper investigates weaving process in the production of security woven wire mesh. Weaving is a critical process of the entire production as the quality of the final product depends very much on this process. High defect rate and low production yield is now a major concern in the production. There has been no prior study of the relationship among variables such as inspection data and machine setting on production yield. Conducting experiments to investigate this relationship is not reasonable in this case, as the product targeted at premium market and scrap cost is very high. In order to investigate the effect of these parameters, artificial neural network (ANN) was applied to model the process with data from the company databases. The type of ANN used in this research was the multi-layer neural network trained with back-propagation algorithm. The results suggested that ANN can effectively be used to predict weaving process production yield. The use of ANN proposed in this research is not limit to only weaving process, but can be applied to other manufacturing process.

scholarly journals Artificial neural network to predict the natural convection from vertical and inclined arrays of horizontal cylinders

2012 ◽  
Vol 14 (4) ◽  
pp. 46-52 ◽  
Author(s):  
Amin Amiri ◽  
Alimohammad Karami ◽  
Tooraj Yousefi ◽  
Mohammad Zanjani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Natural Convection ◽  
Back Propagation ◽  
Diameter Ratio ◽  
Training Data ◽  
Back Propagation Algorithm ◽  
Cylinder Diameter ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract The main focus of the present study is to utilize the artificial neural network (ANN) in predicting the natural convection from horizontal isothermal cylinders arranged in vertical and inclined arrays. The effects of the vertical separation spacing to the cylinder diameter ratio (Py/d), horizontal separation spacing to the cylinder diameter ratio (Px/d) and Rayleigh number (Ra) variation on the average heat transfer from the arrays are considered via this prediction. The training data for optimizing the ANN structure is based on available experimental data. The Levenberg-Marquardt back propagation algorithm is used for ANN training. The proposed ANN is developed using MATLAB functions. For the best ANN structure obtained in this investigation, the mean relative errors of 0.027% and 0.482% were reached for the training and test data, respectively. The results show that the predicted values are very close to the experimental ones.

scholarly journals The Use of Artificial Neural Network to Optimize The pH Response Range of Chlorophenol Red

2017 ◽  
Vol 20 (3&4) ◽  
pp. 253-259 ◽  
Author(s):  
F. Wong ◽  
M. Ahmad ◽  
L. Y. Heng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Ph Indicator ◽  
Ph Response ◽  
Chlorophenol Red ◽  
Response Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) had been used in this study to extend the response range of the pH indicator.  The input from absorbance values of the absorbance spectra of chlorophenol red at different pH was used to train the ANN.  During the training process, the coefficient values of the ANN will be adjusted to obtain the desire output.  In this research, back propagation algorithm had been used for optimizing the response range of the pH indicator chlorophenol red in solution.  The result indicates that the use of ANN enable the pH response range to be extended from 4.8-6.8 to 1.0-10.0.

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