Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting
Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions in optimizing MLP in time series forecasting. This study uses autoregressive integrated moving average (ARIMA) and MLP...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20469/1/23.pdf http://journalarticle.ukm.my/20469/ https://www.ukm.my/jsm/malay_journals/jilid51bil8_2022/KandunganJilid51Bil8_2022.html |
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| Institution: | Universiti Kebangsaan Malaysia |
| Language: | English |
| Summary: | Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer
perceptron (MLP). However, both algorithms have mixed conclusions in optimizing MLP in time series forecasting. This
study uses autoregressive integrated moving average (ARIMA) and MLP with both Levenberg-Marquardt algorithm
and conjugate gradient method. These methods were used to predict the Air Pollutant Index (API) in Malaysia’s central
region where represent urban and residential areas. The performances were discussed and compared using the mean
square error (MSE) and mean absolute percentage error (MAPE). The result shows that MLP models have outperformed
ARIMA models where MLP with Levenberg-Marquardt algorithm outperformed the conjugate gradient method. |
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