scholarly journals PREDIKSI PENGGUNAAN BANDWIDTH MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK

2016 ◽  
Vol 10 (2) ◽  
pp. 127-135
Author(s):  
Jefri Radjabaycolle ◽  
Reza Pulungan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Minimum Error ◽  
Target Error ◽  
Hidden Layer ◽  
Elman Recurrent Neural Network

Jaringan Syaraf Tiruan (JST) sering dipakai dalam menyelesaikan permasalahan tertentu seperti prediksi, klasifikasi, dan pengolahan data. Berdasarkan hal tersebut, dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam prediksi penggunaan bandwidth. Sistem yang dikembangkan dapat digunakan untuk memprediksi pengunaan bandwidth dengan menerapkan Elman Recurrent Neural Network (ERNN). Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi.. Vektor input yang digunakan menggunakan windows size. Hasil penelitian dengan menggunakan target error sebesar 0.001 menunjukkan nilai MSE terkecil yaitu pada windows size 11 dengan nilai 0.002833. Kemudian dengan menggunakan 13 neuron pada hidden layer diperoleh nilai error paling optimal (minimum error) sebesar 0.003725.

scholarly journals Sistem Evaluasi Kelayakan Mahasiswa MagangMenggunakan Elman Recurrent Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 37 ◽  
Author(s):  
Agus Aan Jiwa Permana ◽  
Widodo Prijodiprodjo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recurrent Neural Network ◽  
Skills Training ◽  
Minimum Error ◽  
Training Mode ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Elman Recurrent Neural Network

AbstrakJaringan Syaraf Tiruan (JST) dapat digunakan untuk memecahkan permasalahan tertentu seperti prediksi, klasifikasi, pengolahan data, dan robotik.Berdasarkan paparan tersebut, sehingga dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam program magang yang sedang dihadapi dalam upaya untuk meningkatkan kompetensi, pengalaman, serta melatih softskill mahasiswa.Sistem yang dikembangkan dapat digunakan untuk mengevaluasi kelayakan mahasiswa dalam program magang ke luar daerah dengan menerapkan Elman Recurrent Neural Network (ERNN), sehingga dapat memberikan informasi yang akurat kepada pihak jurusan untuk menentukan keputusan yang tepat.Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi. Adapun metode pembelajaran yang digunakan adalah Backpropagation ThroughTime dengan model epochwise training mode. Sistem diimplementasikan dengan menggunakan bahasa pemrograman C# dengan basis data MySQL. Vektor input yang digunakan terdiri dari 11 variabel. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan akan cepat mengalami konvergen dan mampu mencapai nilai error paling optimal (minimum error) apabila menggunakan 1 hidden layer dengan jumlah neuron 20 unit. Akurasi terbaik dapat diperoleh dengan menggunakan LR sebesar 0.01 dan momentum 0.85 dimana akurasi rata-rata dalam pengujian mencapai 87.50%. Kata kunci—Evaluasi, Kelayakan, Jaringan Syaraf Tiruan (JST), Elman Recurrent Neural Network, Magang Abstract Artificial Neural Network (ANN) can be used to solve specific problems such as prediction, classification, data processing, and robotics. Based on the exposure, so in this study tried to apply neural networks to handle problems in apprentice program facing in an effort to increase the competence, experience and soft skills training students. The system developed can be used to evaluate the students in the apprentice program to other regions by applying the Elman Recurrent Neural Network (ERNN), so it can provide accurate information to the department to determine appropriate decisions. Elman structure was chosen because it can be create much more rapidly iterations so as to facilitate the convergence process. The learning method used is Backpropagation Through Time with model epochwise training mode. The system is implemented using the C # programming language with a MySQL database. Input vector used consists of 11 variables. The results showed that the developed system will rapidly converge and can reach optimal error value (minimum error) when using one hidden layer with 20 units number of neurons. Best accuracy can be obtained using the LR of 0.01 and momentum 0.85 which average accuracy reaches 87.50% in testing. Keywords—Evaluation, Feasibility, Artificial Neural Network (ANN), Elman Recurrent Neural Network, Apprenticeship

scholarly journals ПІДХОДИ ДО СТВОРЕННЯ ПРОГРАМНОГО АГЕНТА НА ОСНОВІ МЕТАЕВРИСТИК І ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ

2019 ◽  
pp. 58-65
Author(s):  
Євген Євгенович Федоров ◽  
Марина Володимирівна Чичужко ◽  
Владислав Олегович Чичужко
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Internal State ◽  
Numerical Study ◽  
Agent Based ◽  
Development Framework ◽  
Number Of Visits ◽  
Artificial Neural ◽  
The Creation ◽  
Elman Recurrent Neural Network

In this article, has been developed a software agent based on meta-heuristics and artificial neural networks. The analysis of existing classes of agents and the selected reactive agent with internal state, which is well suited for partially observable, dynamic and non-episodic media, was carried out, and this agent has an internal state that preserves the state of the environment, obtained on the basis of the history of acts of perception, in the form of structured data. Were proposed approaches to create an agent based on meta-heuristics and an agent based on an artificial neural network. The development of reactive agents with internal state, based on the PSO (particle swarm optimization) metaheuristics, which are related to individual particles and to a whole swarm and interact by messages was proposed. Also, has been proposed an approach to the creation of a reactive agent with an internal state based on the Elman recurrent neural network. The agent-based approach allows combining different areas of artificial intelligence, digital signal processing, mathematical modeling, and game theory. The proposed agents were implemented using the JADE (Java Agent Development Framework) toolkit, which is one of the most popular tools for the creation of agent systems. A numerical study was made to determine the parameters of the swarm PSO metaheuristics and the Elman recurrent neural network. As a purpose function, the Rastrigin test function has been used. The number of visits to the website of DonNTU was used as an input sample for the Elman network. The minimum average square error forecast was the criterion for choosing the structure of a network model. 10 hiding neurons were used to predict the number of visits to the website page, since, with increasing of hidden neurons number, the change in the error value is small. To determine the number of particles in the swarm, a series of experiments was conducted, the results of which are presented by graphs. The proposed approaches can be used in intelligent computer systems.

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