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ITS » Undergraduate Theses » Sistem Informasi - S1
Posted by dewi007 at 13/05/2009 15:53:13  •  5948 Views


PENERAPAN JARINGAN SYARAF TIRUAN RUNGE KUTTA UNTUK IDENTIFIKASI SISTEM DINAMIK NONLINEAR

IMPLEMENTATION OF RUNGE KUTTA NEURAL NETWORK FOR NONLINEAR DYNAMIC SYSTEM IDENTIFICATION

Author :
Susanti, Andini Retno 




ABSTRAK

Jaringan Syaraf Tiruan JST seperti Multi Layer Perceptron MLP telah lama digunakan untuk mengidentifikasi sistem dinamik nonlinear yang digambarkan lewat persamaan diferensial biasa. Hasil identifikasi dengan MLP hanya dapat memprediksi perilaku sistem dengan baik pada selang waktu yang tetap namun tidak begitu akurat untuk selang waktu yang berbeda. Oleh karena itu diusulkan JST yang dapat melakukan prediksi yang lebih akurat pada persoalan sistem dinamik. JST ini dirangkai berdasarkan metode Runge Kutta. JST Runge Kutta yang akan dibahas berupa Runge Kutta Multi Layer Perceptron RKMLP. RKMLP merupakan gabungan dari beberapa MLP yang dirangkai berdasarkan metode Runge Kutta orde empat. Pembelajaran pada RKMLP menggunakan teknik gradient descent. Pengujian dilakukan pada 6 kasus yakni persamaan Van der Pol benda jatuh Lorenz One-Link Robot Arm persamaan Duffing dan reaktor kimia. Untuk membuktikan hasil identifikasi yang baik dengan RKMLP maka digunakan MLP sebagai pembanding. Dapat disimpulkan bahwa hasil identifikasi dengan RKMLP lebih akurat dibandingkan dengan MLP.


ABSTRACT

Neural network like Multi Layer Perceptron MLP has been using for identifying the nonlinear dynamic system depicted by pass the ordinary differential equation. The result of identifying by MLP can only predict the behavioral of system better at a time gap which remain but do not be accurate so to be different a time gap. Because of that neural network which can predict more accurately is proposed to solve problem of dynamic system. Neural network is connected based on Runge Kutta method. Runge Kutta neural network which will discuss is Runge Kutta Multi Layer Perceptron RKMLP. RKMLP is the combination from some MLP which connected based on Runge Kutta fourth-order. Training on RKMLP by using gradient descent technique. Testing held on 6 cases there are Vander Pols equation falling body lorenz one-link robot arm duffing equation and chemical reactor. For approving result of good identifying by using RKMLP we use MLP as comparison. So we can conclude that identification result with RKMLP is more accurately compared by MLP.



Keywordsidentifikasi sistem; sistem nonlinear; metode Runge Kutta; Multi Layer Perceptron; gradient descent
 
Subject:  jaringan saraf tiruan
Contributor
  1. Wiwik Anggraeni, S.Si, M.Kom
    Rully Soelaiman, S.Kom, M.Kom
Date Create: 13/05/2009
Type: Text
Format: pdf.
Language: Indonesian
Identifier: ITS-Undergraduate-3100008033033
Collection ID: 3100008033033
Call Number: RSSI 006.32 Sus p


Source
Undergraduate theses of Information System Engineering, RSSI 006.32 Sus p, 2008

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