scholarly journals Predicting the Number of COVID-19 Sufferers in Malang City Using the Backpropagation Neural Network with the Fletcher–Reeves Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Syaiful Anam ◽  
Mochamad Hakim Akbar Assidiq Maulana ◽  
Noor Hidayat ◽  
Indah Yanti ◽  
Zuraidah Fitriah ◽  
...  
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Gradient Descent ◽  
Infected Individual ◽  
Prediction Method ◽  
Optimization Method ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Backpropagation Neural Network ◽  
Congenital Diseases

COVID-19 is a type of an infectious disease that is caused by the new coronavirus. The spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for sufferers with congenital diseases and a weak immune system. COVID-19 spreads through direct contact, wherein the infected individual spreads the COVID-19 virus through cough, sneeze, or close contacts. Predicting the number of COVID-19 sufferers becomes an important task in the effort to curb the spread of COVID-19. Artificial neural network (ANN) is the prediction method that delivers effective results in doing this job. Backpropagation, a type of ANN algorithm, offers predictive problem solving with good performance. However, its performance depends on the optimization method applied during the training process. In general, the optimization method in ANN is the gradient descent method, which is known to have a slow convergence rate. Meanwhile, the Fletcher–Reeves method has a faster convergence rate than the gradient descent method. Based on this hypothesis, this paper proposes a prediction model for the number of COVID-19 sufferers in Malang using the Backpropagation neural network with the Fletcher–Reeves method. The experimental results show that the Backpropagation neural network with the Fletcher–Reeves method has a better performance than the Backpropagation neural network with the gradient descent method. This is shown by the Means Square Error (MSE) resulting from the proposed method which is smaller than the MSE resulting from the Backpropagation neural network with the gradient descent method.

scholarly journals Fractional-Order Deep Backpropagation Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhui Bao ◽  
Yifei Pu ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fractional Order ◽  
Bp Neural Network ◽  
Gradient Descent ◽  
Necessary Conditions ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Backpropagation Neural Network ◽  
Bp Neural Network Model

In recent years, the research of artificial neural networks based on fractional calculus has attracted much attention. In this paper, we proposed a fractional-order deep backpropagation (BP) neural network model with L2 regularization. The proposed network was optimized by the fractional gradient descent method with Caputo derivative. We also illustrated the necessary conditions for the convergence of the proposed network. The influence of L2 regularization on the convergence was analyzed with the fractional-order variational method. The experiments have been performed on the MNIST dataset to demonstrate that the proposed network was deterministically convergent and can effectively avoid overfitting.

scholarly journals IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD

2021 ◽  
pp. 5-13
Author(s):  
Kseniia Bazilevych ◽  
Ievgen Meniailov ◽  
Dmytro Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Gradient Descent ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Adaptive Boosting ◽  
Software Product ◽  
Boosting Method

Subject: the use of the mathematical apparatus of neural networks for the scientific substantiation of anti-epidemic measures in order to reduce the incidence of diseases when making effective management decisions. Purpose: to apply cluster analysis, based on a neural network, to solve the problem of identifying areas of incidence. Tasks: to analyze methods of data analysis to solve the clustering problem; to develop a neural network method for clustering the territory of Ukraine according to the nature of the epidemic process COVID-19; on the basis of the developed method, to implement a data analysis software product to identify the areas of incidence of the disease using the example of the coronavirus COVID-19. Methods: models and methods of data analysis, models and methods of systems theory (based on the information approach), machine learning methods, in particular the Adaptive Boosting method (based on the gradient descent method), methods for training neural networks. Results: we used the data of the Center for Public Health of the Ministry of Health of Ukraine distributed over the regions of Ukraine on the incidence of COVID-19, the number of laboratory examined persons, the number of laboratory tests performed by PCR and ELISA methods, the number of laboratory tests of IgA, IgM, IgG; the model used data from March 2020 to December 2020, the modeling did not take into account data from the temporarily occupied territories of Ukraine; for cluster analysis, a neural network of 60 input neurons, 100 hidden neurons with an activation Fermi function and 4 output neurons was built; for the software implementation of the model, the programming language Python was used. Conclusions: analysis of methods for constructing neural networks; analysis of training methods for neural networks, including the use of the gradient descent method for the Adaptive Boosting method; all theoretical information described in this work was used to implement a software product for processing test data for COVID-19 in Ukraine; the division of the regions of Ukraine into zones of infection with the COVID-19 virus was carried out and a map of this division was presented.

scholarly journals An Adaptive Neural Network Controller Based on PSO and Gradient Descent Method for PMSM Speed Drive

2018 ◽  
Vol 9 (3) ◽  
pp. 1412
Author(s):  
Zribi Ali ◽  
Zaineb Frijet ◽  
Mohamed Chtourou
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Control Method ◽  
Speed Control ◽  
Pso Algorithm ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), a new adaptive speed control method for a permanent magnet synchronous motor (PMSM) is proposed. Firstly, PSO algorithm is adopted to get the best set of weights of neural network controller (NNC) for accelerating the convergent speed and preventing the problems of trapping in local minimum. Then, to achieve high-performance speed tracking despite of the existence of varying parameters in the control system, gradient descent method is used to adjust the NNC parameters. The stability of the proposed controller is analyzed and guaranteed from Lyapunov theorem. The robustness and good dynamic performance of the proposed adaptive neural network speed control scheme are verified through computer simulations.

Wireless Brain Wave Classification for Alzheimer’s Patients via Efficient Neural Network Computation

2018 ◽  
Vol 10 (03) ◽  
pp. 1850004
Author(s):  
Grant Sheen
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Descent Method ◽  
Stochastic Gradient Descent ◽  
Normal Person ◽  
Gradient Descent Method ◽  
Step Size ◽  
Brain Wave ◽  
Proposed Model ◽  
Wireless Recording

Wireless recording and real time classification of brain waves are essential steps towards future wearable devices to assist Alzheimer’s patients in conveying their thoughts. This work is concerned with efficient computation of a dimension-reduced neural network (NN) model on Alzheimer’s patient data recorded by a wireless headset. Due to much fewer sensors in wireless recording than the number of electrodes in a traditional wired cap and shorter attention span of an Alzheimer’s patient than a normal person, the data is much more restrictive than is typical in neural robotics and mind-controlled games. To overcome this challenge, an alternating minimization (AM) method is developed for network training. AM minimizes a nonsmooth and nonconvex objective function one variable at a time while fixing the rest. The sub-problem for each variable is piecewise convex with a finite number of minima. The overall iterative AM method is descending and free of step size (learning parameter) in the standard gradient descent method. The proposed model, trained by the AM method, significantly outperforms the standard NN model trained by the stochastic gradient descent method in classifying four daily thoughts, reaching accuracies around 90% for Alzheimer’s patient. Curved decision boundaries of the proposed model with multiple hidden neurons are found analytically to establish the nonlinear nature of the classification.

scholarly journals Support Vector Machine optimization with fractional gradient descent for data classification

2021 ◽  
Vol 2 (1) ◽  
pp. 1-6
Author(s):  
Dian Puspita Hapsari ◽  
Imam Utoyo ◽  
Santi Wulan Purnami
Keyword(s):  
Gradient Descent ◽  
Large Scale ◽  
Computing Time ◽  
Data Classification ◽  
Optimization Method ◽  
Descent Method ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Gradient Descent Method

Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.

Data-Driven Approach for Equipment Reliability Prediction Using Neural Network

2011 ◽  
Vol 411 ◽  
pp. 563-566 ◽  
Author(s):  
Feng Ding ◽  
Xing Ben Han
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gradient Descent ◽  
Optimization Theory ◽  
Descent Method ◽  
Numerical Control ◽  
Data Driven ◽  
Gradient Descent Method ◽  
Training Algorithm ◽  
Cnc Machine

BP neural network based data-driven method is proposed to predict reliability in this paper. The BP neural network prediction using Gradient Descent Method (GDM), Additional Momentum Gradient Descent Method (AMGDM) and Levenberg-Marquardt Method(L-M) based on numerical optimization theory of training algorithm are compared with different neuron number. The proposed approach is validated via age data collected from computer numerical control (CNC) machine tool in the field. The results from the proposed method show that perfect predicting performance is achieved under considering selecting suitable number of the hidden neurons and training algorithm. Remarks are outlined regarding the fact that BP neural network based on data-driven method is feasible, effective and adequate predicting accuracy can be obtained.

Sign in / Sign up

Export Citation Format

Share Document