scholarly journals Identification Of Number Using Artificial Neural Network Backpropagation

2018 ◽  
Vol 215 ◽  
pp. 01011
Author(s):  
Sitti Amalia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Simulation Test ◽  
Backpropagation Algorithm ◽  
Network Parameter ◽  
Artificial Intelligent ◽  
Design And Implementation ◽  
Network Training ◽  
Artificial Neural

This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI FREKUENSI RESONANSI ANTENA MIKROSTRIP

2019 ◽  
Vol 12 (1) ◽  
pp. 33-40
Author(s):  
Khairi Budayawan ◽  
Yuhandri Yuhandri ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antennas ◽  
Training Data ◽  
Backpropagation Algorithm ◽  
Neural Network Learning ◽  
Artificial Neural Network Method ◽  
Dimensional Parameters ◽  
Artificial Neural

The resonant frequency of an antenna is determined by the dimensional parameters and permittivity of the antenna substrate. Generally, to get the resonant frequency, a complex mathematical formula is needed to solve. For this reason, an intelligent method is offered to determine the resonant frequency more easily. In this study, an artificial neural network method with Backpropagation algorithm is used to overcome the problem. The data used were consisting of 80 training data and 15 testing data. The results have shown that the artificial neural network learning method with the backpropagation algorithm was successfully utilized to calculate the resonant frequency of microstrip antennas, where the precision of the resonant frequency obtained of 93.33% at an error of = 1%, and 100% at an error of = 2%.

scholarly journals Implementation of Product Sales Forecast Using Artificial Neural Network Method

2021 ◽  
Vol 5 (2) ◽  
pp. 153
Author(s):  
Cholid Fauzi ◽  
Aly Dzulfikar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Process ◽  
Backpropagation Algorithm ◽  
Network Training ◽  
Leaf Springs ◽  
Artificial Neural ◽  
Forecasting Analysis ◽  
Product Sales ◽  
Network Pattern

Product sales forecasting is used by companies to estimate or predict future sales levels using sales data in the previous year. The Artificial Neural Network Backpropagation Algorithm can forecast the sales of goods for the next period for each item in the company. The forecasting process begins by determining the variables needed in the network pattern, and then the established network pattern continued in the network training process using the backpropagation algorithm. After carrying out the network training process, the researcher comparisons with several network patterns formed. This research was conducted to discuss the forecasting analysis of PT XYZ products on spiral and leaf springs. Forecasting carried out on Toyota 48210-25290 R3 type leaf springs using the Artificial Neural Network Backpropagation method with a learning rate weight value of 0.1 hidden layers four and an error of 0.01. From the data processing analysis that has been carried out based on the weight parameters selected, the prediction of sales in April.

scholarly journals IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI KELULUSAN MAHASISWA

2018 ◽  
Vol 5 (2) ◽  
pp. 169
Author(s):  
Muhammad Dedek Yalidhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Prediction Accuracy ◽  
Confusion Matrix ◽  
The Other ◽  
Training Data ◽  
Backpropagation Algorithm ◽  
Testing Data ◽  
Artificial Neural

<p><em>Student’s graduation is one kind of the college accreditation elements by BAN-PT. Because of that. Information System is one of the department in STMIK Banjarbaru, there is no application has been implemented to predict imprecisely of student’s graduation time so far, which causes on time graduation percentage tend low every year. Therefore the accurate student’s graduation prediction can help the committe to choose the correct decisions in order to prevent the imprecisely of student’s graduation time. In this research, the backpropagation algorithm of artificial neural network will be implemented into the application with the output result as delayed and on time graduation. This reseach is using 318 data samples which the 70 % of it will be used as the training data and the other 30 % will be used as testing data. From the calculation of confusion matrix table’s the percentage of the prediction accuracy is 98.97 %.</em></p><p><em></em><em><strong>Keywords</strong>: student’s graduation, artificial neural network, backpropagation, confusion matrix</em></p><p><em></em><em>Kelulusan mahasiswa merupakan salah satu elemen dalam standar akreditasi perguruan tinggi oleh BAN-PT. Sistem Informasi adalah salah satu program studi yang ada di STMIK Banjarbaru, selama ini belum ada aplikasi yang diimplementasikan untuk memprediksi ketidaktepatan waktu kelulusan mahasiswanya yang menyebabkan angka kelulusan tepat waktu cenderung rendah setiap tahunnya. Oleh sebab itu, prediksi kelulusan mahasiswa yang akurat dapat membantu pihak Program Studi dalam mengambil keputusan-keputusan yang tepat untuk mencegah ketidaktepatan waktu kelulusan mahasiswanya. Pada penelitian ini, artificial neural network algoritma backpropagation diimplementasikan pada aplikasi yang dibuat dengan output lulus terlambat dan lulus tepat waktu. Penelitian ini menggunakan sebanyak 318 sampel data yang mana 70 % data digunakan sebagai data training dan 30 % data digunakan sebagai data testing. Dari hasil perhitungan tabel confusion matrix diperoleh persentase akurasi prediksi sebesar 98.97 %.</em></p><p><em></em><em><strong>Kata kunci</strong>: kelulusan mahasiswa, artificial neural network, backpropagation, confusion matrix</em></p>

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

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