DOI: 10.5937/jaes18-25687
This is an open access article distributed under the CC BY 4.0
Volume 18 article 698 pages: 338 - 345
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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