A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Implementasi Jaringan Syaraf Tiruan Recurrent Menggunakan Gradient Descent Adaptive Learning Rate and Momentum Untuk Pendugaan Curah Hujan
The artificial neural network (ANN) technology in rainfall prediction can be done using the learning approach. The ANN prediction accuracy is measured by the determination coefficient (R2) and root mean square error (RMSE). This research implements Elman’s Recurrent ANN which is heuristically optimized based on el-nino southern oscilation (ENSO) variables: wind, southern oscillation index (SOI), sea surface temperatur (SST) dan outgoing long wave radiation (OLR) to forecast regional monthly rainfall in Bongan Bali. The heuristic learning optimization done is basically a performance development of standard gradient descent learning algorithm into training algorithms: gradient descent momentum and adaptive learning rate. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 leap 74,6% while the second data group that is 50% training data and 50% testing data produce the maximum R2 leap 49,8%.
Implementasi Jaringan Syaraf Tiruan Recurrent Menggunakan Gradient Descent Adaptive Learning Rate and Momentum Untuk Pendugaan Curah Hujan
The artificial neural network (ANN) technology in rainfall prediction can be done using the learning approach. The ANN prediction accuracy is measured by the determination coefficient (R2) and root mean square error (RMSE). This research implements Elman’s Recurrent ANN which is heuristically optimized based on el-nino southern oscilation (ENSO) variables: wind, southern oscillation index (SOI), sea surface temperatur (SST) dan outgoing long wave radiation (OLR) to forecast regional monthly rainfall in Bongan Bali. The heuristic learning optimization done is basically a performance development of standard gradient descent learning algorithm into training algorithms: gradient descent momentum and adaptive learning rate. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 leap 74,6% while the second data group that is 50% training data and 50% testing data produce the maximum R2 leap 49,8%.
Implementasi Jaringan Syaraf Tiruan Recurrent Menggunakan Gradient Descent Adaptive Learning Rate and Momentum Untuk Pendugaan Curah Hujan
Afan Galih Salman (author) / Yen Lina Prasetio (author)
2011
Article (Journal)
Electronic Resource
Unknown
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