Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes18-25687
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 18 article 698 pages: 338 - 345

Siwaporn Kunnapapdeelert*
Burapha University, International College, Logistics management, Chonburi, Thailand

Sirinthorn Thepmongkorn
Burapha University, International College, International business management, Chonburi, Thailand

Shipping volume in Thailand have signifi cantly 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, fi nancial 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 confi rms 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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