PENDETEKSIAN DERAU CITRA SECARA OTOMATIS MENGGUNAKAN TEKNIK JARINGAN SYARAF TIRUAN

This research was conducted to investigate the ability of backpropagation artificial neural network in estimating rainfall. Neural network used consists of input layer, 2 hidden layers and output layer. Input layer consists of 12 neurons that represent each input; first hidden layer consists of 12 neurons with activation function tansig, while the second hidden layer consists of 24 neurons with activation function logsig. Output layer consists of 1 neuron with activation function purelin. Training method used is the method of gradient descent with momentum. Training method used is the method of gradient descent with momentum. Learning rate and momentum parameters defined respectively by 0.1 and 0.5. To evaluate the performance of the network model to recognize patterns of rainfall data is used in Biak city rainfall data from January 1997 - December 2008 (12 years). This data is divided into 2 parts, namely training and testing data using rainfall data from January 1997-December 2005 and data estimation using rainfall data from January 2006-December 2008. From the results of this study concluded that rainfall patterns Biak town can be recognized quite well by the model of back propagation neural network. The test results and estimates of the model results testing the value of R = 0.8119, R estimate = 0.53801, MAPE test = 0.1629, and MAPE estimate = 0.6813.

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Modelling of the Parameters of EDM in Gas Based on Back Propagation Neural Network

Materials Science Forum ◽

10.4028/www.scientific.net/msf.926.11 ◽

2018 ◽

Vol 926 ◽

pp. 11-16

Author(s):

Yan Cherng Lin ◽

Han Ming Chow ◽

Hsin Min Lee ◽

Jia Feng Liu

Keyword(s):

Neural Network ◽

Prediction Models ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Machining Parameters ◽

Ann Model ◽

Output Layer ◽

Input Layer ◽

Hidden Layer ◽

Machining Characteristics

The aim of this study is to develop a predicted model of the machining parameters with relation to material removal rate (MRR) and surface roughness (SR) of electrical discharge machining (EDM) in gas. The experimental tasks were implemented by a specific design of experimental method named central composite design (CCD) method. The mathematical prediction models between operating parameters and machining characteristics based on artificial neural network (ANN) were established. The back propagation neural network (BPNN) was employed to construct the architecture of the input layer, the hidden layer and the output layer to build the ANN model. Moreover, the weight and the bias values were examined by the steepest descent method (SDM) with the training data. Thus, the suitable ANN models were established with the acquired weight and bias values. The essential parameters of the EDM in gas such as peak current (Ip), pulse duration (tp), gas pressure (GP), servo reference voltage (Sv) were chosen to investigate the effects on MRR and SR. The developed ANN model with 4 input variables on the input layer, one hidden layer with 5 neurons, and 2 response variables on the output layer was obtained by the training with 30 experimental data. Moreover, as the prediction values obtained from the ANN compared with the 5 testing data, the error falls in the rage of 5% indicating the developed ANN is appropriate and predictable. Moreover, the developed ANN model can be used to predict the machining characteristics such as MRR and SR for the EDM in gas with various parameter settings.

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The effect of Kurtosis on the accuracy of artificial neural network predictive model

MATEC Web of Conferences ◽

10.1051/matecconf/201820402018 ◽

2018 ◽

Vol 204 ◽

pp. 02018

Author(s):

Aisyah Larasati ◽

Anik Dwiastutik ◽

Darin Ramadhanti ◽

Aal Mahardika

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Predictive Models ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Misclassification Rates ◽

Hidden Layer ◽

Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

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Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network

Volume 2: I&C, Digital Controls, and Influence of Human Factors; Plant Construction Issues and Supply Chain Management; Plant Operations, Maintenance, Aging Management, Reliability and Performance; Renewable Energy Systems: Solar, Wind, Hydro and Geothermal; Risk Management, Safety and Cyber Security; Steam Turbine-Generators, Electric Generators, Transformers, Switchgear, and Electric BOP and Auxiliaries; Student Competition; Thermal Hydraulics and Computational Fluid Dynamics ◽

10.1115/power-icope2017-3280 ◽

2017 ◽

Author(s):

Chang Guo ◽

Ming Gao ◽

Peixin Dong ◽

Yuetao Shi ◽

Fengzhong Sun

Keyword(s):

Neural Network ◽

Network Model ◽

Working Conditions ◽

Bp Neural Network ◽

Centrifugal Pumps ◽

Output Layer ◽

Sample Data ◽

Input Layer ◽

Flow Induced Noise ◽

Hidden Layer

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

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

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400001827 ◽

1997 ◽

Vol 11 (1) ◽

pp. 33-44 ◽

Cited By ~ 11

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.

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PATTERN RECOGNITION AKSARA LAMPUNG MENGGUNAKAN ALGORITMA NEURAL NETWORK

Jurnal Informasi dan Komputer ◽

10.35959/jik.v9i2.264 ◽

2021 ◽

Vol 9 (2) ◽

pp. 116-121

Author(s):

Nopiyanto . ◽

Rahmadi Rahmadi

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Error Rate ◽

Output Layer ◽

Input Layer ◽

Hidden Layer

Indonesia merupakan Negara yang terdiri dari berbagai macam suku dan budaya, Indonesia juga memiliki berbagai macam bahasa daerah, salah satunya merupakan bahasa Lampung. Bahasa Lampung merupakan bahasa asli suku lampung, didalam bahasa lampung terdapat aksara yaitu aksara lampung. Aksara lampung memiliki 20 kepala bahasa dan 12 tanda baca. Pada penelitian ini dilakukan analisa pengenalan tulisan berdasarkan perubahan iterasi dengan menggunakan metode neural network. Neural network merupakan jaringan saraf yang terdiri dari unit dasar yang seperti analog dengan neuron, neural network dibagi berdasarkan 3 layer yaitu input layer, hidden layer dan output layer. Dimana setiap node pada masing-masing layer memiliki suatu error rate, yang akan digunakan untuk proses training. Pada penelitian ini akan menggunakan bahasa pemograman python. Percobaan untuk induk surat akan menggunakan 20 huruf aksara lampung dengan masing-masing huruf terdapat 10 pengujian citra, dan percobaan untuk anak surat akan menggunakan 12 huruf anak aksara lampung dengan masing-masing huruf terdapat 10 pengujian citra, sehingga total keseluruhan dataset mejadi 320 citra. Hasil yang diperoleh dari proses pemeriksaan masing-masing adalah 75%, untuk induk surat dengan sebaran 135 citra terdeteksi benar dan 45 citra tidak terdeteksi dengan benar. Untuk anak surat 81 citra terdeteksi dengan benar dan 27 citra tidak terdeteksi dengan benar.

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Design and Implementation of the Multifunction Fast Charger for LiFePO4 Battery

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.577.568 ◽

2014 ◽

Vol 577 ◽

pp. 568-571

Author(s):

Wen Yeau Chang

Keyword(s):

Neural Network ◽

Pulse Width Modulation ◽

Rbf Neural Network ◽

State Of Charge ◽

Neural Network Approach ◽

Output Layer ◽

Input Layer ◽

Battery State Of Charge ◽

Estimation System ◽

Hidden Layer

The paper proposes a multifunction charger for LiFePO4 battery. The proposed multifunction charger contains charge circuit for LiFePO4 battery and state of charge (SOC) estimation system. With a micro processor integrated, the proposed charger is capable of digital control to improve the system reliability. The fast charger uses pulse current charging circuit which integrates micro processor, voltage detecting interface, current detecting interface and control software. The user can adjust the duty cycle and frequency of charging pulse by controlling the pulse width modulation (PWM) signal. The proposed battery state of charge estimation system is based on the radial basis function (RBF) neural network approach. The architecture of RBF neural network used in this project contains an input layer, an output layer and a hidden layer. Input layer has 3 neurons which are impedance, voltage, current of battery and output layer has one neuron which is SOC. To verify the performances of the proposed multifunction fast charger, a prototype charger has been tested on practical LiFePO4 battery. The field practical operations of the prototype charger were studied in accordance with the conditions for the different charging modes.

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Effect of Learning Rate on the Recognition of Images

Active and Passive Electronic Components ◽

10.1155/1996/45086 ◽

1996 ◽

Vol 19 (1) ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

M. Hamed ◽

A. El Desouky

Keyword(s):

Neural Network ◽

Texture Classification ◽

Back Propagation ◽

Learning Rate ◽

Computational Time ◽

Rate Parameter ◽

Modern Computer ◽

The Neural Network ◽

Hidden Layer ◽

Propagation Technique

This paper presents a study for the effect of learning rate on an approach for texture classification and detection based on the neural network principle. This neural network consists of three layers, which are input, output, and hidden layers. The back propagation technique is considered. A computer algorithm is deduced and applied. In this work, the synthetic textures are generated. The results are taken for the modern computer of AT 486 type. The mathematical analysis is summarized in order to illustrate the effect of learning rate parameter on the exact discrimination during processing. This effect is studied through applications. The minimum consumed time for the computational time of classification in industry is correlated to correspond only the use of only 2 units in the hidden layer of a neural network for real images instead of 11 units.

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Pengujian Algoritma Artificial Neural Network (ANN) Untuk Prediksi Kecepatan Angin

Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) ◽

10.32672/jnkti.v2i1.1056 ◽

2019 ◽

Vol 2 (1) ◽

pp. 43

Author(s):

Syukri Syukri ◽

Samsuddin Samsuddin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

Angin memiliki peran yang penting dalam kehidupan manusia, antara lain pada pembangkit listrik, pelayaran dan penerbangan. Ketiga sektor tersebut erat kaitannya dengan kondisi angin. Angin dapat muncul setiap saat dan setiap waktu serta perubahan geografis pada suatu wilayah. Hal ini mengakibatkan sulitnya menentukan kecepatan angin, maka untuk mengatasi masalah tersebut diperlukan prediksi kecepatan angin. Saat ini berbagai metode prediksi telah banyak dikembangkan, salah satu metode yang dapat digunakan untuk melakukan prediksi dengan akurasi yang tinggi yaitu algoritma Artificial Neural Network (ANN) Backpropagation. Arsitektur ANN yang digunakan adalah 4 parameter input layer, hidden layer (5, 10, 15, 20, 25 dan 30) dan output layer (1 parameter). Data pembelajaran dan pengujian didapatkan dari stasiun BMKG Blang Bintang Aceh Besar, berupa data kecepatan angin jam per harian periode Januari 2011 sampai dengan Desember 2015 yang terdiri dari arah angin, suhu, tekanan, kelembaban dan suhu. Hasil pengujian menunjukkan bahwa metode ANN Backpropagation cukup baik diterapkan untuk proses prediksi, kemampuan ANN dalam melakukan prediksi memiliki tingkat akurasi rata – rata yang lebih baik yaitu 96 %. Sedangkan nilai rata – rata kerapatan daya angin jam per harian yaitu 45.030 W/m2

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PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT

Jurnal Gaussian ◽

10.14710/j.gauss.v7i1.26636 ◽

2018 ◽

Vol 7 (1) ◽

pp. 64-72

Author(s):

Ekky Rosita Singgih Wigati ◽

Budi Warsito ◽

Rita Rahmawati

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Processing System ◽

Training Data ◽

Neural Network Modeling ◽

Feed Forward Neural Network ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Hidden Layer

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE

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