scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

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%.

Back Propagation Method of Artificial Neural Networks for Finding the Position Control of Stanford Manipulator and Direct Kinematic Analysis of Elbow Manipulator

2018 ◽  
Vol 7 (1) ◽  
pp. 46 ◽  
Author(s):  
M. Sailaja ◽  
R. D. V. Prasad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Network Approach ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Direct Kinematics

Nowadays the robot technology is advancing rapidly and the use of robots in industries has been increasing. In designing a robot manipulator, kinematicsplays a vital role. The kinematic problem of manipulator control is divided into two types, direct kinematics and inverse kinematics. Robot inverse kinematics, which is important in robot path planning, is a fundamental problem in robotic control. Past solutions for this problem have been through the use of various algebraic or algorithmic procedures, which may be less accurate and time consuming. Artificial neural networks have the ability to approximate highly non-linear functions applied in robot control. The neural network approach deserves examination because of the fundamental properties of computation speed, and they can generalize untrained solutions. In the present work an attempt has been made to evaluate the problemof robot inverse kinematics of Stanford manipulator using artificial neural network approach. Finally two programs are written using C language to solve inverse kinematic problem of Stanford manipulator using Back propagation method of artificial neural network. In this network, the input layer has six nodes, the hidden layer has three nodes, and the output layer has two nodes. And also Elbow manipulator was modelled and its direct kinematics was analysed.

Use of Artificial Neural Networks for Calculating Elasticity Modulus of Construction Layers of Non-Rigid Road Surfacing Based on Natural Data

2020 ◽  
pp. 111-120
Author(s):  
М. М. М. Елшами ◽  
А. Н. Тиратурян ◽  
А. Н. Канищев
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Elastic Moduli ◽  
Back Propagation ◽  
Data Set ◽  
Artificial Neural ◽  
Instrumental Recording ◽  
Accuracy Of Prediction

Постановка задачи. Рассматриваются вопросы использования искусственных нейронных сетей при решении задач обработки результатов инструментальных регистраций чаш прогибов нежесткой дорожной одежды с использованием установок ударного нагружения FWD . Результаты. Проведен анализ и отмечены недостатки существующих методов обработки экспериментальных чаш прогибов, в частности метода обратного расчета модулей упругости слоев дорожных одежд, заключающиеся в длительном времени выполнения расчетов и неустойчивости получаемых результатов. Построена структура искусственной нейронной сети для определения модулей упругости слоев дорожной одежды. Обучение искусственной нейронной сети осуществлялось с использованием метода обратного распространения ошибки. Выводы. Разработанная нейронная сеть продемонстрировала хорошие результаты при обучении по тестовому набору данных, а также высокую точность прогнозирования модулей упругости слоев дорожных одежд. Statement of the problem. The article is devoted to the use of artificial neural networks in solving the problems of processing the results of instrumental recording of bowls of deflections of non-rigid road surfacing using FWD shock loading settings. Results. The analysis was carried out, the shortcomings of the existing processing methods were identified, in particular the backcalculation method, which involves a long calculation time, and the instability of the results obtained. The structure of the artificial neural network was designed to determine the elastic moduli of the pavement layers. Training of an artificial neural network was carried out using the method of back propagation of error. Conclusions. The developed neural network has shown good results in training on the test data set, as well as high accuracy of prediction of the elastic moduli of the pavement.

Damage Identification of Rock Mass with Artificial Neural Networks

2007 ◽  
Vol 353-358 ◽  
pp. 2325-2328
Author(s):  
Zi Chang Shangguan ◽  
Shou Ju Li ◽  
Mao Tian Luan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Damage Identification ◽  
Back Propagation ◽  
Training Time ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
High Degree

The inverse problem of rock damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Convergence measurements of displacements at a few of positions are used to determine the location and magnitude of the damaged rock in the excavation disturbed zones. Unlike the classical optimum methods, ANN is able to globally converge. However, the most frequently used Back-Propagation neural networks have a set of problems: dependence on initial parameters, long training time, lack of problemindependent way to choose appropriate network topology and incomprehensive nature of ANNs. To identify the location and magnitude of the damaged rock using an artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy.

scholarly journals Determining the Number of Neurons in Artificial Neural Networks for Approximation, Trained with Algorithms Using the Jacobi Matrix

TEM Journal ◽  
2020 ◽  
pp. 1320-1329
Author(s):  
Kostadin Yotov ◽  
Emil Hadzhikolev ◽  
Stanka Hadzhikoleva
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Jacobi Matrix ◽  
Optimal Number ◽  
Upper Limit ◽  
Artificial Neural ◽  
Hidden Layer

How can we determine the optimal number of neurons when constructing an artificial neural network? This is one of the most frequently asked questions when working with this type of artificial intelligence. Experience has brought the understanding that it takes an individual approach for each task to specify the number of neurons. Our method is based on the requirement of algorithms looking for a minimum of functions of type 𝑺􁈺𝒛􁈻 􀵌 Σ 􁈾𝝋𝒊 𝒎 􁈺𝒛 􁈻􁈿𝟐 𝒊􀭀𝟏 that satisfy the inequality 𝒑 􀵑 𝒎, where p is the dimensionality of the argument z, and m is the number of functions. Formulas for an upper limit of the required neurons are proposed for networks with one hidden layer and for networks with r hidden layers with an equal number of neurons.

scholarly journals IDENTIFIKASI POLA KARAKTER ANAK DENGAN ALGORITMA PERCEPTRON

2019 ◽  
Vol 3 (1) ◽  
pp. 86
Author(s):  
Sri Rahmadhany
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Computational Method ◽  
Input Output ◽  
Perceptron Algorithm ◽  
Behavioral Variables ◽  
Artificial Neural

Abstract - Artificial Neural Network is a computational method that works like a human brain. The Perceptron algorithm is one method that exists in Artificial Neural Networks. The research carried out was the identification of children's character patterns using the Perceptron algorithm. The Perceptron algorithm is very reliable in recognizing patterns, one of which is the child's character pattern as was done in this study. The Perceptron algorithm identifies the character patterns of children through three inputs and two outputs. The three outputs are taken from nature variables, attitude variables and behavioral variables. The output is four human temperaments according to Hipocrates, namely sanguin, melancholy, choleric and plegamatic. All inputs and outputs will be converted into binary numbers to be trained with Matlab software.Keywords - Artificial Neural Networks, Perceptron Algorithms, child character patterns, input, output, binary numbers. Abstrak - Jaringan Syaraf Tiruan merupakan salah satu metode komputasi yang dapat bekerja seperti layaknya otak manusia. Algortima Perceptron merupakan salah satu metode yang ada pada Jaringan Syaraf Tiruan. Penelitian yang dilakukan adalah identifikasi pola karakter anak dengan menggunakan algoritma Perceptron. Algoritma Perceptron sangat handal dalam mengenali pola salah satunya yaitu pola karakter anak seperti yang dilakukan dalam penelitian ini. Algoritma Perceptron mengidentifikasi pola karakter anak melalui tiga input dan dua output. Tiga output tersebut diambil dari variabel sifat, variabel sikap dan variabel tingkah laku. Adapun output merupakan empat temperamen manusia menurut Hipocrates yaitu sanguin, melankolis, koleris dan plegamatis. Seluruh input dan output akan diubah menjadi bilangan biner untuk dilatih dengan software Matlab.Kata Kunci - Jaringan Syaraf Tiruan, Algoritma Perceptron, pola karakter anak, input, output, bilangan biner.

scholarly journals Jaringan Syaraf Tiruan Untuk Pengenalan Citra Sidik Jari Pada Smart Home Berbasis MYSQL

2018 ◽  
Vol 5 (5) ◽  
pp. 597
Author(s):  
Nur Yanti ◽  
Fathur Zaini Rachman ◽  
Nurwahidah Jamal ◽  
Era Purwanto ◽  
Fachrurozy Fachrurozy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Data Base ◽  
Monitoring System ◽  
Smart Home ◽  
Fingerprint Image ◽  
Artificial Neural ◽  
Hidden Layer

<p class="Abstrak"> </p><p class="Abstrak">Sistem keamanan yang bertujuan sebagai sistem monitoring pada <em>smart home</em> seperti memonitoring pengguna laboratorium, perpustakaan, atau ruangan penyimpanan dan peminjaman peralatan praktek di program studi suatu kampus, ruang penyimpanan senjata, hingga rumah tinggal, memerlukan sekuritas yang handal untuk memudahkan identifikasi pengguna ruangan atau pencegahan dari tindak pencurian, maka dirancang sistem monitoring melalui pengenalan citra sidik jari menggunakan sensor ZFM60, jaringan syaraf tiruan dan MySQL. Tujuannya agar di dapat pola yang relevan dari citra dan mengeliminasi informasi atau variabel yang tidak relevan. Metode yang digunakan yaitu <em>experimental</em>, terdiri dari pengumpulan data sidik jari, perancangan sistem pengolahan citra, pembuatan dan pengujian <em>hardware</em> dan <em>software</em>, serta implementasi sistem. Hasil proses pengenalan atau klarifikasi citra sidik jari melalui GUI Matlab, nilai <em>error</em> hasil pengolahan dan pelatihan citra sidik jari dengan jaringan syaraf tiruan, digunakan sebagai ciri citra dan disimpan sebagai <em>data base</em> pada MySQL, kemudian dibandingkan dengan nilai <em>error</em> citra sidik jari baru yang di klarifikasi. Nilai citra yang dapat dikenali berada diantara -0,0005 hingga 0,0005, diluar batas tersebut merupakan citra yang tidak dikenali. Selisih (nilai <em>error</em>) antara ciri citra yang tersimpan pada <em>data base</em> dan ciri citra yang diklarifikasi menghasilkan nilai <em>error </em>yang kecil yaitu &lt; 0.0005, menunjukkan jaringan syaraf tiruan <em>backpropagation</em> handal diimplementasikan pada pengenalan sidik jari untuk melatih pola citra dari sidik jari. Konfigurasi jaringan yaitu maksimal <em>epoch</em> = 3000, <em>learning rate</em> = 1, target <em>error</em> = 0.1, <em>hidden layer</em> = 17. Pelatihan jaringan syaraf tiruan pada konfigurasi tersebut menghasilkan nilai <em>error</em> terkecil dari ciri citra sebesar 0.0000085.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong><em> </em></p><p class="Judul2"><em><br /></em></p><p class="Judul2"><em>The security system that aims as a monitoring system in smart home such as monitoring laboratory users, libraries, or storage rooms and borrowing practical equipment in the study program of a campus, weapons storage room, to a residence, requires reliable securities to facilitate identification of room users or prevention from theft, it is designed a monitoring system through fingerprint image recognition using ZFM60 sensors, artificial neural networks and MySQL. The goal is to get relevant patterns from the image and eliminate irrelevant information or variables. The method used is experimental, consisting of fingerprint data collection, image processing system design, hardware and software manufacturing and testing, and system implementation. The result of the process of recognition or clarification of fingerprint images through the Matlab GUI, the error value of processing and training of fingerprint images with artificial neural networks, is used as a feature of the image and stored as a data base on MySQL, then compared with the error value of the new fingerprint image that is clarified. The recognizable image value is between -0,0005 to 0,0005, beyond this limit is an unrecognized image. The difference (error value) between the characteristics of the image stored in the data base and the clarified image feature produces a small error value of &lt;0.0005, indicating a reliable backpropagation artificial neural network is implemented in fingerprint recognition to train the image pattern of fingerprints. Network configuration is maximum epoch = 3000, learning rate = 1, target error = 0.1, hidden layer = 17. Artificial neural network training in the configuration produces the smallest error value of the image characteristics of 0.0000085.</em></p>

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

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