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2020, vol. 18, br. 3, str. 338-345
Thailand port throughput prediction via particle swarm optimization based neural network
(naslov ne postoji na srpskom)
aBurapha University, International College, Logistics Management, Chonburi, Thailand
bBurapha University, International College, International Business Management, Chonburi, Thailand

e-adresasiwaporn.lgm@gmail.com
Ključne reči: port throughput; particle swarm optimization; neural network; logistics and prediction
Sažetak
(ne postoji na srpskom)
Shipping volume in Thailand have significantly increased in last four years. It is important to pay attention to the trend of Thailand port throughput and use as the guideline to prepare for the needs of supporting facilities, infrastructures, financial and human resources. An effective forecasting technique called particle swarm based neural network (PSONN) is developed to estimate Thailand port throughput in this work. The prediction results from PSONN and classical backpropagation training algorithm, backpropagation neural networks (BPNN) were compared. The results shown that PSONN provides more accurate results than BPNN when apply to predict port throughput of Thailand. The mean squared error obtained from PSONN are about 10 times lower than that of BPNN. This confirms that neural network based on PSO training algorithm has better performance and better ability to escape local optimum than that of BPNN.
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O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/jaes18-25687
objavljen u SCIndeksu: 26.09.2020.
metod recenzije: dvostruko anoniman
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