Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density

2017 ◽  
Vol 1 (1) ◽  
pp. 43 ◽  
Author(s):  
Anjar Wanto ◽  
Agus Perdana Windarto ◽  
Dedy Hartama ◽  
Iin Parlina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Population Density ◽  
Network Architecture ◽  
Activation Function ◽  
Sigmoid Function ◽  
Backpropagation Algorithm ◽  
Architecture Model ◽  
Artificial Neural

Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

scholarly journals Jaringan Saraf Tiruan dengan Backpropagation untuk Memprediksi Penyakit Asma

2015 ◽  
Vol 2 (1) ◽  
pp. 28
Author(s):  
Dahriani Hakim Tanjung
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Activation Function ◽  
Learning Rate ◽  
Sigmoid Function ◽  
A Value ◽  
Artificial Neural ◽  
Require Output

Penelitian ini bertujuan untuk memprediksi penyakit asma menggunakan teknik pengenalan pola yaitu jaringan saraf tiruan dengan metode backpropagation. Data penilaian asma mengacu pada riwayat penyakit asma seseorang. Jaringan saraf tiruan dilakukan dengan menentukan jumlah unit untuk setiap lapisan dengan fungsi aktivasi sigmoid biner. Pengujian dilakukan menggunakan perangkat lunak matlab yang diuji dengan beberapa bentuk arsitektur jaringan. Arsitektur dengan konfigurasi terbaik terdiri dari 18 lapisan masukan, 8 lapisan tersembunyi dan 4 lapisan keluaran dengan nilai learning rate sebesar 0.5, nilai toleransi error 0.001, menghasilkan maksimal epoch 4707 dan MSE 0.00100139. MSE berada di bawah nilai error yaitu 0.001, Parameter tersebut dipilih menjadi parameter terbaik karena menghasilkan jumlah iterasi yang memiliki nilai akurasi MSE yang cukup baik, karena nilai MSE paling kecil dari arsitektur yang lain serta nilai MSE dibawah dari nilai error yang ditentukan. Sigmoid Biner Fungsi ini digunakan untuk jaringan saraf yang dilatih dengan menggunakan metode backpropagation. Fungsi sigmoid memiliki nilai range 0 sampai 1. Oleh karena itu, fungsi ini sering digunakan untuk jaringan saraf yang membutuhkan nilai output yang terletak pada interval 0 sampai 1.This study aims to predict asthma using pattern recognition techniques namely artificial neural network with back propagation method. Asthma assessment data refers to a person's history of asthma. Artificial neural network is done by determining the number of units for each layer with binary sigmoid activation function. Testing is done using matlab software being tested with some form of network architecture. Architecture with the best configuration consists of 18 layers of input, 8 hidden layer and output layer 4 with a value of learning rate of 0.5, the error tolerance value 0001, 4707 and resulted in the maximum epoch MSE .00100139. MSE is under the error value is 0.001, the parameter is chosen to be the best parameters for generating the number of iterations that have an accuracy value of MSE is quite good, because the smallest MSE value than other architectures as well as the value of the MSE under a specified error value. Binary sigmoid function is used for neural network trained using the backpropagation method. Sigmoid function has a value in the range 0 to 1. Therefore, this function is often used for neural networks that require output value lies in the interval 0 to 1.

scholarly journals Memprediksi Jumlah Siswa Baru Menggunakan Metode Backpropagation (STUDI KASUS: SMK HARAPAN BANGSA KUALA)

2021 ◽  
Vol 3 (1) ◽  
pp. 10-16
Author(s):  
Kris Jayanti ◽  
Katen Lumbanbatu ◽  
Suci Ramadani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Time Series Data ◽  
Real Data ◽  
Activation Function ◽  
Series Data ◽  
Base Function ◽  
Artificial Neural

Artificial Neural Network (ANN) and time series data can be used for forecasting methods well. Artificial Neural Network is a method whose working principle is adapted from a mathematical model in humans or biological nerves. Neural networks are characterized by; (1) the pattern of connections between neurons (called architecture), (2) determining the weight of the connection (called training or learning), and (3) the activation function. The research objective was to obtain the best artificial neural network architecture, comparing the two methods of Backpropogation Neural Networks with the Radial Base Function Artificial Neural Network (RBF) method. This research is a research using real data (true experimental). This research was conducted at SMK Harapan Bangsa Kuala, which was obtained from 2015 to 2019. The results showed that for one iteration using the backpropagation method the result was 0,378197657 with a squared error 0.143033468, then the results achieved were not in accordance with the target.

scholarly journals Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships

2018 ◽  
Vol 35 (13) ◽  
pp. 2226-2234 ◽  
Author(s):  
Ameen Eetemadi ◽  
Ilias Tagkopoulos
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Gene Networks ◽  
Supplementary Information ◽  
Genome Wide ◽  
Transcriptomics Data ◽  
Artificial Neural

Abstract Motivation Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications. Results We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations. In its core, the GNN maps existing gene regulatory information in its architecture and it uses cell nodes that have been specifically designed to capture the dependencies and non-linear dynamics that exist in gene networks. These two key features make the GNN architecture capable to capture complex relationships without the need of large training datasets. As a result, GNNs were 40% more accurate on average than competing architectures (MLP, RNN, BiRNN) when compared on hundreds of curated and inferred transcription modules. Our results argue that GNNs can become the architecture of choice when building predictors of gene expression from exponentially growing corpus of genome-wide transcriptomics data. Availability and implementation https://github.com/IBPA/GNN Supplementary information Supplementary data are available at Bioinformatics online.

Predicting The Number of Tourists Based on Backpropagation Algorithm

2021 ◽  
Vol 5 (3) ◽  
pp. 439-445
Author(s):  
Dwi Marlina ◽  
Fatchul Arifin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Predictive Accuracy ◽  
Training Process ◽  
Backpropagation Algorithm ◽  
Squared Error ◽  
Artificial Neural ◽  
Target Data

The number of tourists always fluctuates every month, as happened in Kaliadem Merapi, Sleman. The purpose of this research is to develop a prediction system for the number of tourists based on artificial neural networks. This study uses an artificial neural network for data processing methods with the backpropagation algorithm. This study carried out two processes, namely the training process and the testing process with stages consisting of: (1) Collecting input and target data, (2) Normalizing input and target data, (3) Creating artificial neural network architecture by utilizing GUI (Graphical User Interface) Matlab facilities. (4) Conducting training and testing processes, (5) Normalizing predictive data, (6) Analysis of predictive data. In the data analysis, the MSE (Mean Squared Error) value in the training process is 0.0091528 and in the testing process is 0.0051424. Besides, the validity value of predictive accuracy in the testing process is around 91.32%. The resulting MSE (Mean Squared Error) value is relatively small, and the validity value of prediction accuracy is relatively high, so this system can be used to predict the number of tourists in Kaliadem Merapi, Sleman.  

scholarly journals Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance

2019 ◽  
Vol 14 (1) ◽  
pp. 58-79 ◽  
Author(s):  
Gaetano Bosurgi ◽  
Orazio Pellegrino ◽  
Giuseppe Sollazzo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Performance Evaluation ◽  
Network Architecture ◽  
Training Data ◽  
Structural Performance ◽  
Reliable Technique ◽  
Artificial Neural

Artificial Neural Networks represent useful tools for several engineering issues. Although they were adopted in several pavement-engineering problems for performance evaluation, their application on pavement structural performance evaluation appears to be remarkable. It is conceivable that defining a proper Artificial Neural Network for estimating structural performance in asphalt pavements from measurements performed through quick and economic surveys produces significant savings for road agencies and improves maintenance planning. However, the architecture of such an Artificial Neural Network must be optimised, to improve the final accuracy and provide a reliable technique for enriching decision-making tools. In this paper, the influence on the final quality of different features conditioning the network architecture has been examined, for maximising the resulting quality and, consequently, the final benefits of the methodology. In particular, input factor quality (structural, traffic, climatic), “homogeneity” of training data records and the actual net topology have been investigated. Finally, these results further prove the approach efficiency, for improving Pavement Management Systems and reducing deflection survey frequency, with remarkable savings for road agencies.

Multilayer Backpropagation Neural Networks for Implementation of Logic Gates

2021 ◽  
Vol 12 (1) ◽  
pp. 1-12
Author(s):  
Santosh Giri ◽  
Basanta Joshi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Specific Problem ◽  
Logic Gates ◽  
Backpropagation Algorithm ◽  
Processing Elements ◽  
Artificial Neural ◽  
Hidden Neurons

ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained by using a backpropagation algorithm until the model satisfies the predefined error criteria (e) which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively.

scholarly journals Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

2017 ◽  
Vol 1 (1) ◽  
pp. 34 ◽  
Author(s):  
Sandy Putra Siregar ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Backpropagation Algorithm ◽  
Neural Structure ◽  
Data Prediction ◽  
Architectural Models ◽  
Artificial Neural ◽  
North Sumatra

Artificial Neural Networks are a computational paradigm formed based on the neural structure of intelligent organisms to gain better knowledge. Artificial neural networks are often used for various computing purposes. One of them is for prediction (forecasting) data. The type of artificial neural network that is often used for prediction is the artificial neural network backpropagation because the backpropagation algorithm is able to learn from previous data and recognize the data pattern. So from this pattern backpropagation able to analyze and predict what will happen in the future. In this study, the data to be predicted is Human Development Index data from 2011 to 2015. Data sourced from the Central Bureau of Statistics of North Sumatra. This research uses 5 architectural models: 3-8-1, 3-18-1, 3-28-1, 3-16-1 and 3-48-1. From the 5 models of this architecture, the best accuracy is obtained from the architectural model 3-48-1 with 100% accuracy rate, with the epoch of 5480 iterations and MSE 0.0006386600 with error level 0.001 to 0.05. Thus, backpropagation algorithm using 3-48-1 model is good enough when used for data prediction.

scholarly journals ANALISIS ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI TINGKAT KEBERHASILAN PELATIH SEPAKBOLA SEBAGAI SALAH SATU KEMAJUAN OLAHRAGA DI INDONESIA

2019 ◽  
Vol 6 (3) ◽  
pp. 264
Author(s):  
Muhammad Ridwan Lubis
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Technology ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Backpropagation Algorithm ◽  
Network Methods ◽  
Effective Decision ◽  
Artificial Neural

<p><em>The development of sports is an important role of a trainer and the management role that is in it. Determining the success of the trainer by using the criteria of experience, strategy and understanding of the trainer on the mental and spiritual conditions of each player is the first step in achieving success. Research using computational-based information technology is very much developed mainly by using neural network methods. Research using Artificial Neural Networks has been widely used, especially in the field of sports, especially football, including prediction results of soccer matches. In this study, the study of determining the success rate of soccer coaches as one of the advances in Indonesian football sports using the backpropagation algorithm was the goal of researchers to produce an effective decision in determining the success of football sports in Indonesia</em><em>.</em></p><p><em><strong>Keywords</strong></em><em>: Coach, Football, Artificial Neural Network, Backpropagation Indonesian</em> </p><p><em>Perkembangan olahraga merupakan peran penting dari seorang pelatih dan peran manajemen yang ada didalamnya. Menentukan tingkat keberhasilan pelatih dengan menggunakan kriteria pengalaman, strategi dan pemahaman pelatih terhadap kondisi mental dan spiritual setiap pemain merupakan langkah awal dalam mencapai keberhasilan. Penelitian dengan menggunakan teknologi informasi berbasis komputasi sangat banyak dikembangkan terutama dengan menggunakan metode neural network. Penelitian dengan menggunakan Jaringan Saraf Tiruan sudah banyak digunakan terutama  dalam bidang olahraga terutama sepakbola, diantaranya adalah Prediksi hasil pertandingan sepak bola. Pada penelitian ini, penelitian tetang menentukan tingkat keberhasilan pelatih sepakbola sebagai salah satu kemajuan olahraga sepakbola diindonesia menggunakan algoritma backpropagation menjadi tujuan peneliti  untuk menghasilkan sebuah keputusan yang efektif dalam menentukan keberhasilan olahraga sepakbola di indonesia.</em></p><p><em><strong>Kata kunci</strong></em><em>: </em><em>Pelatih, Sepakbola, Jaringan Saraf Tiruan, Backpropagation, Indonesia</em></p>

Neuro-Scientific Analysis of Weights in Neural Networks

2021 ◽  
Author(s):  
Md. Saqib Hasan ◽  
Rukshar Alam ◽  
Muhammad Abdullah Adnan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Weight Distribution ◽  
Network Architecture ◽  
Network Training ◽  
Quantitative Metrics ◽  
Benchmark Datasets ◽  
Weight Initialization ◽  
Artificial Neural

Deep learning is a popular topic among machine learning researchers nowadays, with great strides being made in recent years to develop robust artificial neural networks for faster convergence to a reasonable accuracy. Network architecture and hyperparameters of the model are fundamental aspects of model convergence. One such important parameter is the initial values of weights, also known as weight initialization. In this paper, we perform two research tasks concerned with the weights of neural networks. First, we develop three novel weight initialization algorithms inspired by the neuroscientific construction of the mammalian brains and then test them on benchmark datasets against other algorithms to compare and assess their performance. We call these algorithms the lognormal weight initialization, modified lognormal weight initialization, and skewed weight initialization. We observe from our results that these initialization algorithms provide state-of-the-art results on all of the benchmark datasets. Second, we analyze the influence of training an artificial neural network on its weight distribution by measuring the correlation between the quantitative metrics of skewness and kurtosis against the model accuracy using linear regression for different weight initializations. Results indicate a positive correlation between network accuracy and skewness of the weight distribution but no affirmative relation between accuracy and kurtosis. This analysis provides further insight into understanding the inner mechanism of neural network training using the shape of weight distribution. Overall, the works in this paper are the first of their kind in incorporating neuroscientific knowledge into the domain of artificial neural network weights.

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