scholarly journals Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Adnan A. Ismael ◽  
Saleh J. Suleiman ◽  
Raid Rafi Omar Al-Nima ◽  
Nadhir Al-Ansari
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Mean Square ◽  
The Third ◽  
Testing Stage ◽  
Inclination Angles

AbstractCylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.

Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study

2020 ◽  
Vol 24 (3) ◽  
pp. 243-251
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Mean Square Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coefficient Of Determination ◽  
Hydraulic Structures ◽  
Mean Square ◽  
Feed Forward Back Propagation

Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

Estimation of ambiguous blast-induced ground vibration using intelligent models: A case study

2018 ◽  
Vol 49 (4) ◽  
pp. 147-157 ◽  
Author(s):  
Ragam Prashanth ◽  
DS Nimaje
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Radial Basis Function ◽  
Mean Square Error ◽  
Basis Function ◽  
Back Propagation ◽  
Ground Vibration ◽  
Back Propagation Neural Network ◽  
Mean Square ◽  
Intelligent Models

Blasting is an economical and viable operation for reliable excavation of hard rock in mining and civil construction. An ambiguous ground vibration generated by blasting is unenviable and causes grievous damage to nearby inhabitants, residential premises, and other sensitive sites. Accordingly, the proper assessment of indistinct blast-induced ground vibration is a requisite to pinpoint the safe limits in and around mines. An endeavor has been made in this article to apply four predictive models, namely, support vector machine, feed forward back propagation neural network, cascaded forward back propagation neural network, and radial basis function neural network to estimate the ground vibration caused by blasting operation conducted at Mine-A, India. In this article, a total number of 121 blasting operations with relevant parameters are recorded. The most influential parameters of ground vibration are the number of holes, burden, spacing, hole diameter, hole depth, top stemming, maximum explosive charge per delay, and the distance from blast source, which were considered as input parameters. Ground vibration is measured in terms of peak particle velocity and is considered as output. The performance indicators of constructed network models were chosen as the coefficient of determination (R2), root mean square error, and variance account for. Among all constructed intelligent models, the radial basis function neural network with architecture 8-80-1 and R2 of 0.9918, root mean square error of 4.4076, and variance account for of 99.1800 was found to be optimum. Sensitivity analysis showed that the number of holes, burden, and top stemming are the most effective parameters leads to ground vibration due to blasting.

scholarly journals ON SOME BOUNDS OF THE MINIMUM EDGE DOMINATING ENERGY OF A GRAPH

2021 ◽  
Vol 12 (4) ◽  
pp. 1285-1296
Author(s):  
Dr. Gauri Ghule , Et. al.
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Fuzzy Classifier ◽  
Manual Intervention ◽  
The Neural Network ◽  
Hidden Neurons

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.

Research on Active Suspension System Based on BP and RBF Network Algorithm

2011 ◽  
Vol 230-232 ◽  
pp. 149-153 ◽  
Author(s):  
Chuan Yin Tang ◽  
Guang Yao Zhao ◽  
Yi Min Zhang ◽  
Xiao Yu E
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Degrees Of Freedom ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Rbf Network ◽  
Network Algorithm

A six degrees of freedom half body vehicle suspension system is presented in the paper .The Back Propagation neural network algorithm and the Radial-Basis Function network algorithm is adopted to control the suspension system. With the aid of software Matlab/Simulink , the simulation model is obtained. A great deal of simulation work is done. Simulation results demonstrate that both the designed radius basis function neural network and the back propagation neural network work well for the proposed vehicle suspension model in the paper .

scholarly journals The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach

2017 ◽  
Vol 76 (9) ◽  
pp. 2413-2426 ◽  
Author(s):  
Seef Saadi Fiyadh ◽  
Mohammed Abdulhakim AlSaadi ◽  
Mohamed Khalid AlOmar ◽  
Sabah Saadi Fayaed ◽  
Ako R. Hama ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Back Propagation ◽  
Deep Eutectic Solvents ◽  
Lead Removal ◽  
Mean Square ◽  
Feed Forward Back Propagation ◽  
Artificial Neural Network Ann

Abstract The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10−4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

scholarly journals MODEL ARSITEKTUR NEURAL NETWORK DENGAN BACKPROPOGATION PADA PREDIKSI TOTAL LABA RUGI KOMPREHENSIF BANK UMUM KONVENSIONAL

2018 ◽  
Vol 5 (2) ◽  
pp. 147 ◽  
Author(s):  
Agus Perdana Windarto ◽  
Muhammad Ridwan Lubis ◽  
Solikhun Solikhun
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Financial Services ◽  
Back Propagation ◽  
Mean Square ◽  
Comprehensive Income ◽  
Income Statement ◽  
Architectural Model ◽  
Testing Accuracy ◽  
Architectural Models

<p><em>determine the marketing strategy in increasing the total comprehensive income. This study aims to create the best architectural model using Backpropogation where this model can later be made to make predictions of total comprehensive income. The variable used in this study is the total comprehensive income statement data of PT. Bank Mandiri, Tbk (January - November 2016). Data sourced from the Financial Services Authority (www.ojk.go.id). From a series of trials conducted with 4 architectural models tested, namely 4-25-1; 4-50-1; 4-100-1 and 4-50-75-1, obtained the best architectural model 4-50-1 with Epoch training = 1977, Mean Square Error (MSE) of 0,000997867 with the correctness of testing accuracy reaching 80%.</em></p><p><strong><em>Keywords</em></strong><em>: Artificial Neural Network, Back-propagation, Comprehensive Income, Prediction, Economy, Architecture</em><em> </em></p><p><em>Prediksi total laba rugi komprehensif sangatlah penting untuk memprediksi dimana posisi angka total laba rugi komprehensif pada suatu bank.  Informasi tersebut berguna bagi masayarkat dalam menentukan arah investasi masyarakat ke depan, begitu juga bagi pihak bank berguna untuk menentukan kebijakan strategi pemasaran dalam meninggkatkan total laba komprehensif tersebut. Penelitian ini bertujuan untuk membuat model arsitektur terbaik dengan menggunakan Backpropogation dimana model ini nantinya dapat dilakukan untuk membuat prediksi terhadap total laba rugi komprehensif. Variabel yang digunakan pada penelitian ini adalah data total laba rugi komprehensif PT. Bank Mandiri,Tbk (Januari – November 2016). Data bersumber dari Otoritas Jasa Keuangan (<a href="http://www.ojk.go.id/">www.ojk.go.id</a>). Dari serangkaian uji coba yang dilakukan dengan 4 model arsitektur yang diuji yakni 4-25-1; 4-50-1; 4-100-1 dan 4-50-75-1, diperoleh model arsitektur terbaik 4-50-1 dengan </em><em>Epoch training = 1977</em><em>, </em><em>Mean Square Error (MSE) sebesar </em><em>0,000997867 dengan </em><em>tingkat akurasi pengujian mencapai kebenaran 80%.</em><em> </em></p><p><strong><em>Kata kunci</em></strong><em>: Jaringan saraf tiruan, Back-propagation, Laba Rugi Komprehensif, Prediksi, Ekonomi, Arsitektur</em></p>

scholarly journals COSTAL BATHYMETRY ESTIMATION FROM MULTISPECTRAL IMAGE WITH BACK PROPAGATION NEURAL NETWORK

2016 ◽  
Vol XLI-B8 ◽  
pp. 1123-1125 ◽  
Author(s):  
S. Y. Huang ◽  
C. L. Liu ◽  
H. Ren
Keyword(s):  
Neural Network ◽  
Coastal Area ◽  
Satellite Images ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Large Area ◽  
Learning Stage ◽  
Training Samples ◽  
Testing Stage ◽  
Mean Square Errors

Bathymetric data in coastal area are important for marine sciences, hydrological applications and even for transportation and military purposes. Compare to traditional sonar and recent airborne bathymetry LIDAR systems, optical satellite images can provide information to survey a large area with single or multiple satellite images efficiently and economically. And it is especially suitable for coastal area because the penetration of visible light in water merely reaches 30 meters. In this study, a three-layer back propagation neural network is proposed to estimate bathymetry. In the learning stage, some training samples with known depth are adopted to train the weights of the neural network until the stopping criterion is satisfied. The spectral information is sent to the input layer and fits the true water depth with the output. The depths of training samples are manually measured from stereo images of the submerged reefs after water refraction correction. In the testing stage, all non-land pixels are processed. The experiments show the mean square errors are less than 3 meters.

scholarly journals COSTAL BATHYMETRY ESTIMATION FROM MULTISPECTRAL IMAGE WITH BACK PROPAGATION NEURAL NETWORK

2016 ◽  
Vol XLI-B8 ◽  
pp. 1123-1125
Author(s):  
S. Y. Huang ◽  
C. L. Liu ◽  
H. Ren
Keyword(s):  
Neural Network ◽  
Coastal Area ◽  
Satellite Images ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Large Area ◽  
Learning Stage ◽  
Training Samples ◽  
Testing Stage ◽  
Mean Square Errors

Bathymetric data in coastal area are important for marine sciences, hydrological applications and even for transportation and military purposes. Compare to traditional sonar and recent airborne bathymetry LIDAR systems, optical satellite images can provide information to survey a large area with single or multiple satellite images efficiently and economically. And it is especially suitable for coastal area because the penetration of visible light in water merely reaches 30 meters. In this study, a three-layer back propagation neural network is proposed to estimate bathymetry. In the learning stage, some training samples with known depth are adopted to train the weights of the neural network until the stopping criterion is satisfied. The spectral information is sent to the input layer and fits the true water depth with the output. The depths of training samples are manually measured from stereo images of the submerged reefs after water refraction correction. In the testing stage, all non-land pixels are processed. The experiments show the mean square errors are less than 3 meters.

scholarly journals FPGA Based Design of Artificial Neural Processor Used for Wireless Sensor Network

2019 ◽  
Vol 7 (1) ◽  
pp. 200-222
Author(s):  
Azzad Bader Saeed ◽  
Sabah Abdul-Hassan Gitaffa
Keyword(s):  
Mean Square Error ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
High Speed ◽  
Intelligent System ◽  
Back Propagation ◽  
Optimization Method ◽  
Back Propagation Neural Network ◽  
Mean Square ◽  
Powerful Learning

In this paper,  a simulation of  artificial intelligent system has been designed for processing  the incoming data of  sensor  units and then presenting proper decision. The Back-propagation Neural Network BPNN has been used as the proposed  intelligent system for this work, whereas the BPNN is considered as a trained network in conjunction with an optimization method for changing the weights and biases of the overall network. The main two features of the  BPNN are: high speed processing, and producing  lowest Mean-Square-Error MSE ( cost function ) in few iterations. The proposed BPNN has used the linear activation functions 'Satlins' and 'Satline' for the hidden and output layer respectively, and has used the training function 'Traingda' ( which is gradient descent with adaptive learning rate)  as a powerful learning method. It is worth to mention, that no previous research used these three functions together for such analysis. The MATLAB software package has been used for  designing and testing the proposed system. An optimal result has been obtained in this work, where the value of  Mean-Square-Error has reached to zero   in 87 epochs, and the real and desired outputs have been fitted. In fact, there is  no previous work has reached to this optimal result.  The proposed BPNN has been implemented in FPGA, which is fast, and low power tool.

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