scholarly journals PATTERN RECOGNITION AKSARA LAMPUNG MENGGUNAKAN ALGORITMA NEURAL NETWORK

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.

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

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.

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

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.

scholarly journals PENDETEKSIAN DERAU CITRA SECARA OTOMATIS MENGGUNAKAN TEKNIK JARINGAN SYARAF TIRUAN

2009 ◽  
Vol 1 (2) ◽  
pp. 73-86
Author(s):  
Julsam Julsam
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Convolution Operation ◽  
Output Layer ◽  
Output Neuron ◽  
Input Layer ◽  
Image Blurring ◽  
Hidden Layer ◽  
Propagation Technique ◽  
Blurring Effect

This research is application of neural network technique to optimize convolution operation using mask 3x3 to omit the image blurring effect. This neural network consists of three layers.  They are input layer (9 neuron inputs), output layer (1 output neuron) and hidden layer. Each layer is applied to 3, 5 and 7 neuron using back propagation technique. The result shows the using five neurons to hidden layer give the highest value of sound pixel recognizing (76.47%)

scholarly journals Perancangan Model Jaringan Syaraf Tiruan Propagasi Balik Untuk Mengestimasi Curah Hujan

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Elohansen Padang
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Back Propagation ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Rainfall Data ◽  
Training Method ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer

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.

Design and Implementation of the Multifunction Fast Charger for LiFePO4 Battery

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.

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

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.

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

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

<span lang="EN-US">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 <em>Artificial Neural Network</em> (ANN) <em>Backpropagation</em>. Arsitektur ANN yang digunakan adalah  4 parameter <em>input layer</em>, <em>hidden layer</em> (5, 10, 15, 20, 25 dan 30) dan <em>output layer</em> (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 <em>Backpropagation </em>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 </span><span lang="EN-US">45.030 W/m<sup>2</sup></span>

scholarly journals DETEKSI KUALITAS TEMBAKAU BERDASARKAN AROMA BERBASIS ELECTRONIC NOSE

2021 ◽  
Vol 16 (1) ◽  
pp. 37
Author(s):  
Misbah Misbah ◽  
Nurul Arif ◽  
Yoedo Ageng Suryo
Keyword(s):  
Neural Network ◽  
Electronic Nose ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer

Tembakau mempunyai aroma khas, yang dihasilkan dari bahan organik yang mudah menguap dan yang tidakmudah menguap. Kualitas tembakau ditentukan dari proses fermentasi dan pengeringan. Pada industri rokok,penentuan kualitas tembakau dilakukan oleh tenaga ahli dengan mengandalkan indra penciuman. Hal iniberpotensi menghasilkan tingkat kesalahan yang tinggi. Electronic nose dapat dijadikan salah satu solusidalam menentukan kualitas tembakau. Electronic nose terdiri dari beberapa sensor gas dan unit pengolah data. Sensor gas yang dipakai adalah MQ4, MQ7, MQ 135 dan MQ137. Sedangkan pada unit pengolah dataterdapat algoritma kecerdasan buatan menggunakan neural network. Neural network terdiri dari 4 neuron pada input layer, 25 neuron pada hidden layer dan 2 neuron pada output layer dengan fungsi aktivasi TanSig. Dari hasil pengujian sistem ini dapat mengidentifikasi tembakau yang baik, sedang dan jelek dengan tingkatkeakurasiaan 95%.

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

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 

A Kind of Text Classification Method Based on Fuzzy Vector Space Model and Neural Networks

2013 ◽  
Vol 347-350 ◽  
pp. 2856-2859
Author(s):  
Jun Hui Pan ◽  
Hui Li
Keyword(s):  
Neural Networks ◽  
Vector Space ◽  
Text Classification ◽  
Vector Space Model ◽  
Classification Method ◽  
Space Model ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer ◽  
Fuzzy Vector

A kind of text classification method based on fuzzy vector space model and neural networks is proposed in the paper according to the problems that a text can be belongs to many types during the text classification. Fuzzy theory is adopted in the method to look the occurring position of feature items in text on as the important degree (membership) reflecting text subject, and fully considered the position information while the features are extracted, thus the fuzzy feature vectors are constructed, as a result, the text classification is close to the manual classification method. The established networks are constituted of input layer, hidden layer and output layer, the input layer completes the inputs of classification samples, hidden layer extracts the implicit pattern features of input samples, the output layer is used to output the classification results. Finally the effectiveness of this method is proved by some documents of Wan Fang data in experimental section. (Abstract)

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