scholarly journals Computational Intelligent Paradigms to Solve the Nonlinear SIR System for Spreading Infection and Treatment Using Levenberg–Marquardt Backpropagation

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 618
Author(s):  
Muhammad Umar ◽  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Manoj Gupta ◽  
Dac-Nhuong Le ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Scheme ◽  
System Of Differential Equations ◽  
Reference Dataset ◽  
Testing Procedures ◽  
The Neural Network ◽  
Levenberg Marquardt ◽  
Numerical Computing ◽  
The Mean ◽  
Computational Intelligent

The current study aims to design an integrated numerical computing-based scheme by applying the Levenberg–Marquardt backpropagation (LMB) neural network to solve the nonlinear susceptible (S), infected (I) and recovered (R) (SIR) system of differential equations, representing the spreading of infection along with its treatment. The solutions of both the categories of spreading infection and its treatment are presented by taking six different cases of SIR models using the designed LMB neural network. A reference dataset of the designed LMB neural network is established with the Adam numerical scheme for each case of the spreading infection and its treatment. The approximate outcomes of the SIR system based on the spreading infection and its treatment are presented in the training, authentication and testing procedures to adapt the neural network by reducing the mean square error (MSE) function using the LMB. Studies based on the proportional performance and inquiries based on correlation, error histograms, regression and MSE results establish the efficiency, correctness and effectiveness of the proposed LMB neural network scheme.

scholarly journals A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Qi ◽  
Yanan Zhao ◽  
Yufang Huang ◽  
Yang Wang ◽  
Wei Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Phones ◽  
Mean Value ◽  
Wheat Cultivars ◽  
N Content ◽  
Color Parameters ◽  
The Neural Network ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

AbstractThe accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

2021 ◽  
Author(s):  
Daniel Roten ◽  
Kim B. Olsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Site Response ◽  
Site Amplification ◽  
Data Driven ◽  
Vertical Array ◽  
The Neural Network ◽  
Simplifying Assumptions ◽  
The Mean ◽  
Fully Connected

ABSTRACT We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK-net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data-driven prediction of site response that is independent of proxies or simplifying assumptions.

scholarly journals Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks

2021 ◽  
Vol 12 (4) ◽  
pp. 178
Author(s):  
Gilles Van Van Kriekinge ◽  
Cedric De De Cauwer ◽  
Nikolaos Sapountzoglou ◽  
Thierry Coosemans ◽  
Maarten Messagie
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Negative Impact ◽  
Absolute Error ◽  
Electric Grid ◽  
Electric Vehicle Charging ◽  
The Neural Network ◽  
The Mean ◽  
Ev Charging ◽  
Charging Demand

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW.

A Genetic Algorithm to Improve a Neural Network to Predict a Patient’s Response to Warfarin

1993 ◽  
Vol 32 (01) ◽  
pp. 55-58 ◽  
Author(s):  
M. N. Narayanan ◽  
S. B. Lucas
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Predictive Ability ◽  
Predictor Variables ◽  
Optimal Subset ◽  
The Neural Network ◽  
International Normalised Ratio ◽  
The Mean ◽  
Paired T Test

Abstract:The ability of neural networks to predict the international normalised ratio (INR) for patients treated with Warfarin was investigated. Neural networks were obtained by using all the predictor variables in the neural network, or by using a genetic algorithm to select an optimal subset of predictor variables in a neural network. The use of a genetic algorithm gave a marked and significant improvement in the prediction of the INR in two of the three cases investigated. The mean error in these cases, typically, reduced from 1.02 ± 0.29 to 0.28 ± 0.25 (paired t-test, t = −4.71, p <0.001, n = 30). The use of a genetic algorithm with Warfarin data offers a significant enhancement of the predictive ability of a neural network with Warfarin data, identifies significant predictor variables, reduces the size of the neural network and thus the speed at which the reduced network can be trained, and reduces the sensitivity of a network to over-training.

scholarly journals Computational Fluid Dynamic Design and Experimental Study of a Temperature Sensor Array Used in Climate Reference Station

2019 ◽  
Vol 36 (9) ◽  
pp. 1835-1847
Author(s):  
Jie Yang ◽  
Qingquan Liu ◽  
Wei Dai
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Air Temperature ◽  
Neural Network Model ◽  
Temperature Sensor ◽  
Sensor Array ◽  
The Neural Network ◽  
Temperature Observation ◽  
The Mean ◽  
Cfd Method

Accurate air temperature measurements are demanded for climate change research. However, air temperature sensors installed in a screen or a radiation shield have traditionally resisted observation accuracy due to a number of factors, particularly solar radiation. Here we present a novel temperature sensor array to improve the air temperature observation accuracy. To obtain an optimum design of the sensor array, we perform a series of analyses of the sensor array with various structures based on a computational fluid dynamics (CFD) method. Then the CFD method is applied to obtain quantitative radiation errors of the optimum temperature sensor array. For further improving the measurement accuracy of the sensor array, an artificial neural network model is developed to learn the relationship between the radiation error and environment variables. To assess the extent to which the actual performance adheres to the theoretical CFD model and the neural network model, air temperature observation experiments are conducted. An aspirated temperature measurement platform with a forced airflow rate up to 20 m s−1 served as an air temperature reference. The average radiation errors of a temperature sensor equipped with a naturally ventilated radiation shield and a temperature sensor installed in a screen are 0.42° and 0.23°C, respectively. By contrast, the mean radiation error of the temperature sensor array is approximately 0.03°C. The mean absolute error (MAE) between the radiation errors provided by the experiments and the radiation errors given by the neural network model is 0.007°C, and the root-mean-square error (RMSE) is 0.009°C.

Study on Glass Furance Temperature Dual Control Based on Neural Networks Decoupling Control under WINCC

2013 ◽  
Vol 341-342 ◽  
pp. 856-860
Author(s):  
Hao Ming Yang ◽  
Lan Qing Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Decoupling Control ◽  
Dual Control ◽  
Script Language ◽  
Learning Speed ◽  
The Neural Network ◽  
Levenberg Marquardt ◽  
Experiment Control

Experiment control platform for the neural network decoupling control is constructed for the glass furnace taking heavy oil as fuel. By dual control, the improving Levenberg-Marquardt learning algorithm is discussed in order to improve the learning speed and to satisfy the real control. The neural network decoupling real control based on C-Script language and PLC S7-400 hard system under WINCC is realized with satisfying control results.

scholarly journals Image correction for cone-beam computed tomography simulator using neural network corrector

2017 ◽  
Vol 9 (2) ◽  
pp. 168781401769047
Author(s):  
Chin-Sheng Chen ◽  
Cheng-Yi Hsu ◽  
Shih-Kang Chen ◽  
Chih-Jer Lin ◽  
Ching-Hao Hsieh ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Learning Algorithm ◽  
Central Point ◽  
Cone Beam ◽  
Measure Data ◽  
Image Correction ◽  
The Neural Network ◽  
Levenberg Marquardt

In this article, a neural network corrector is proposed to correct the image shift, yielding the degradation of three-dimensional image reconstruction, for each slice captured by cone-beam computed tomography simulator. There are 3 degrees of freedom in tube module of simulator; the central point of tube module should be aligned with the central point of detector module to guarantee the accurate image projection. However, the mechanism manufacturing and assembling tolerance will let the above aim cannot be met. Here, a standard kit is made to measure the image shift by 1° step from −10° to 10°. The measure data will be the input training data of proposed neural network corrector, and the corrected translation position will be the output of neural network corrector. The Levenberg–Marquardt learning algorithm adjusts the connected weights and biases of the neural network using a supervised gradient descent method, such that the defined error function can be minimized. To avoid the problem of overfitting and improve the generalized ability of the neural network, Bayesian regularization is added to the Levenberg–Marquardt learning algorithm. After the training of neural network corrector, the different target position commands are fed into the neural network corrector. Then, the corrected data from neural network corrector are fed to be the new position command to verify the image correction performance. Moreover, a phantom kit is made to check the corrected performance of the neural network corrector. Finally, the experimental results verify that the image shift can be reduced by the neural network corrector.

Optimization of the Neural Network by Using the LabVIEW Instrumentation

2012 ◽  
Vol 463-464 ◽  
pp. 1011-1016 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Dan Paune ◽  
Oprean Aurel
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Error Function ◽  
Virtual Instrumentation ◽  
Closed Loops ◽  
Network Parameters ◽  
The Neural Network ◽  
On Line ◽  
The Mean ◽  
Number Of Iterations

The paper shown one assisted method to construct simple and complex neural network and to simulate on-line them. By on-line simulation of some more important neural simple and complex network is possible to know what will be the influences of all network parameters like the input data, weight, biases matrix, sensitive functions, closed loops and delay of time. There are shown some important neurons type, transfer functions, weights and biases of neurons, and some complex layers with different type of neurons. By using the proper virtual LabVIEW instrumentation in on-line using, were established some influences of the network parameters to the number of iterations before canceled the mean square error to the target. Numerical simulation used the proper teaching law and proper virtual instrumentation. In the optimization step of the research on used the minimization of the error function between the output and the target.

scholarly journals Penerapan Fitur Warna Untuk Identifikasi Plasmodium Falciparum pada Sediaan Apus Darah Menggunakan MK-Means dan Jaringan Backpropagation

MATICS ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 70
Author(s):  
Mustamin Hamid
Keyword(s):  
Neural Network ◽  
Plasmodium Falciparum ◽  
Blood Smear ◽  
Test Results ◽  
Specific Data ◽  
Color Features ◽  
The Neural Network ◽  
Average Accuracy ◽  
The Mean ◽  
Background Image

<p><strong>Abstract - This research proposed a system to identify Plasmodium falciparum on blood smear  using the neural network  backpropagation. Modified K-Means (MK-Means) is used to separate between the object with the background image because that method was able to equalize the value of fitness at all Center cluster so there is no dead center and can also cope with the local minimum value. The extraction of the features used in this study consists of color features i.e. calculation of the mean, standard deviation, skewness, curtosis and entropy of co-occurent matrix with the purpose to get the values of all the trait value image, obtained are then used to train a neural network with the backpropagation training algorithm. Method of backpropagation networks capable of acquiring knowledge even though there is no certainty, able to perform a generalization and extraction of a specific data pattern.</strong></p><p><strong>                        </strong><strong>The image of  the preparations  blood smear  are classified using the method of  neural network Backpropagation. The test results obtained from Tropozoit with the accuracy 100%, scizon 80% and gametocytes 80%. Identification is then obtained outcomes the introduction with an average accuracy of 86,66%.</strong></p>

scholarly journals Neural networks application to determine the types and magnitude of aberrations from the pattern of the point spread function out of the focal plane

2021 ◽  
Vol 2086 (1) ◽  
pp. 012148
Author(s):  
P A Khorin ◽  
A P Dzyuba ◽  
P G Serafimovich ◽  
S N Khonina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Point Spread Function ◽  
Focal Plane ◽  
Prediction Errors ◽  
Point Spread ◽  
The Neural Network ◽  
Spread Function ◽  
The Mean

Abstract Recognition of the types of aberrations corresponding to individual Zernike functions were carried out from the pattern of the intensity of the point spread function (PSF) outside the focal plane using convolutional neural networks. The PSF intensity patterns outside the focal plane are more informative in comparison with the focal plane even for small values/magnitudes of aberrations. The mean prediction errors of the neural network for each type of aberration were obtained for a set of 8 Zernike functions from a dataset of 2 thousand pictures of out-of-focal PSFs. As a result of training, for the considered types of aberrations, the obtained averaged absolute errors do not exceed 0.0053, which corresponds to an almost threefold decrease in the error in comparison with the same result for focal PSFs.

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