Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression
| Autor: | Wannarat Suntiamorntut, Edy Fradinata, Sakesun Suthummanon |
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| Rok vydání: | 2018 |
| Předmět: |
Optimization
SVR General Computer Science Mean squared error Artificial neural network business.industry Supervised learning Activation function Process (computing) Pattern recognition Sigmoid function Function (mathematics) Supervised Support vector machine MSE Artificial intelligence Electrical and Electronic Engineering business ANN Mathematics |
| Popis: | This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR. |
| Databáze: | OpenAIRE |
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