Results of Fitted Neural Network Models on Malaysian Aggregate Dataset
| Autor: | Hishamuddin Hashim, Nor Azura Md Ghani, Saadi Ahmad Kamaruddin, Ismail Musirin |
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| Rok vydání: | 2018 |
| Předmět: |
Aggregate
Control and Optimization Artificial neural network Autoregressive Moving Computer Networks and Communications Computer science Aggregate (data warehouse) Average (NARMA) Backpropagation Neural network Set (abstract data type) Hardware and Architecture Control and Systems Engineering Section (archaeology) Consistency (statistics) Statistics Computer Science (miscellaneous) Autoregressive (NAR) Electrical and Electronic Engineering Instrumentation Physics::Atmospheric and Oceanic Physics Ordinary Nonlinear Information Systems |
| Zdroj: | Bulletin of Electrical Engineering and Informatics. 7:272-278 |
| ISSN: | 2302-9285 2089-3191 |
| DOI: | 10.11591/eei.v7i2.1177 |
| Popis: | This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures. This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset. |
| Databáze: | OpenAIRE |
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