scholarly journals Prediction of Dengue Hemorrhagic Fever Cases Based on Weather Parameters Using Back Propagation Neural Networks (Case Study in Pontianak City)

2019 ◽  
Vol 15 (2) ◽  
pp. 114-121
Author(s):  
I T Rahayu ◽  
N Nurhasanah ◽  
R Adriat
Keyword(s):  
Neural Networks ◽  
Dengue Hemorrhagic Fever ◽  
Strong Correlation ◽  
Network Architecture ◽  
Back Propagation ◽  
Hemorrhagic Fever ◽  
Activation Function ◽  
Neuron Network ◽  
Back Propagation Neural Networks ◽  
Weather Parameters

Research has been conducted by predicting cases of dengue hemorrhagic fever based on weather parameters. The data used are weather parameters in the form of air temperature data, air humidity, rainfall, duration of solar radiation and wind speed as input data and data on dengue hemorrhagic fever cases as the target data. This study aims to see the confirmation of dengue hemorrhagic parameters in Pontianak. The benefit in the field of education is that students and teachers are aware of the dangers of dengue because it can cause death. The method used is back propagation neural networks with the best network architecture in predicting cases of dengue hemorrhagic fever are [50 40 30 1] and binary sigmoid activation function, bipolar sigmoid and linear function. The activation function will determine whether the signal from the neuron input will be forwarded to other neurons and is also used to determine the output of a neuron. Network training correlation value is 0.9995 (very strong correlation) with MSE 0.0001 and network testing is 0.9325 (very strong correlation) with MSE 1.61. Determination coefficient serve as accuracy with values obtained is 0.85, which means that 85% of weather parameters can be used as input in predicting the incidence of dengue hemorrhagic fever in Pontianak City.

Selecting the architecture of a class of back-propagation neural networks used as approximators

1997 ◽  
Vol 11 (1) ◽  
pp. 33-44 ◽  
Author(s):  
William C. Carpenter ◽  
Margery E. Hoffman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Back Propagation ◽  
Region Of Interest ◽  
Input Output ◽  
Advantages And Disadvantages ◽  
Back Propagation Neural Networks ◽  
Input Layer ◽  
Hidden Layer

AbstractThis paper examines the architecture of back-propagation neural networks used as approximators by addressing the interrelationship between the number of training pairs and the number of input, output, and hidden layer nodes required for a good approximation. It concentrates on nets with an input layer, one hidden layer, and one output layer. It shows that many of the currently proposed schemes for selecting network architecture for such nets are deficient. It demonstrates in numerous examples that overdetermined neural networks tend to give good approximations over a region of interest, while underdetermined networks give approximations which can satisfy the training pairs but may give poor approximations over that region of interest. A scheme is presented that adjusts the number of hidden layer nodes in a neural network so as to give an overdetermined approximation. The advantages and disadvantages of using multiple output nodes are discussed. Guidelines for selecting the number of output nodes are presented.

Guidelines for the selection of network architecture

1997 ◽  
Vol 11 (5) ◽  
pp. 395-408 ◽  
Author(s):  
William C. Carpenter ◽  
Margery E. Hoffman
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Back Propagation ◽  
Test Functions ◽  
Back Propagation Neural Networks ◽  
Numerous Test ◽  
Hidden Layer ◽  
Selection Of

AbstractThis paper is concerned with presenting guidelines to aide in the selection of the appropriate network architecture for back-propagation neural networks used as approximators. In particular, its goal is to indicate under what circumstances neural networks should have two hidden layers and under what circumstances they should have one hidden layer. Networks with one and with two hidden layers were used to approximate numerous test functions. Guidelines were developed from the results of these investigations.

Neural networks and Sigmoid Activation Function in Multi-Layer Networks

2020 ◽  
Vol 1 (2) ◽  
pp. 29-43
Author(s):  
Renas M Redwan
Keyword(s):  
Neural Networks ◽  
Logic Gate ◽  
Back Propagation ◽  
Main Idea ◽  
Activation Function ◽  
Back Propagation Neural Networks ◽  
Linear Threshold ◽  
Non Linear ◽  
Hidden Layer ◽  
Sigmoid Activation Function

Back propagation neural networks are known for computing the problems that cannot easily be computed (huge datasets analysis or training) in artificial neural networks. The main idea of this paper is to implement XOR logic gate by ANNs using back propagation neural networks for back propagation of errors, and sigmoid activation function. This neural networks to map non-linear threshold gate. The non-linear used to classify binary inputs ( ) and passing it through hidden layer for computing  and  ( ), after computing errors by ( ) the weights and thetas ( ) are changing according to errors. Sigmoid activation function is =  and Derivation of sigmoid is = . The sig(x) and Dsig(x) is between 1 to 0.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

A Reverse Approach in Optimizing Pass Parameters

2010 ◽  
Vol 113-116 ◽  
pp. 1707-1711
Author(s):  
Jian Hua Hu ◽  
Yuan Hua Shuang
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Learning Process ◽  
Back Propagation ◽  
Fem Simulation ◽  
Steel Pipe ◽  
Good Convergence ◽  
Back Propagation Neural Networks ◽  
Element Method

A method combines a back propagation neural networks (BPNN) with the data obtained using finite element method (FEM) is introduced in this paper as an approach to solve reverse problems. This paper presents the feasibility of this approach. FEM results are used to train the BPNN. Inputs of the network are associated with dimension deviation values of the steel pipe, and outputs correspond to its pass parameters. Training of the network ensures low error and good convergence of the learning process. At last, a group of optimal pass parameters are obtained, and reliability and accuracy of the parameters are verified by FEM simulation.

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