Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia
| Autor: | Silfi Iriyani, Dinda Lestarini, Taufiqurrahman Taufiqurrahman, Sarifah Putri Raflesia |
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| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Control and Optimization Artificial neural network Computer Networks and Communications Computer science Process (engineering) business.industry Commodity Particle swarm optimization Distribution (economics) Exchange rate Artificial Intelligence Hardware and Architecture Control and Systems Engineering Agriculture Computer Science (miscellaneous) Electrical and Electronic Engineering Robustness (economics) business Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Informatics (IJEEI). 9 |
| ISSN: | 2089-3272 |
| Popis: | Agricultural commodity price forecasting becomes important for farmers since the knowledge of agriculture commodity price fluctuation can help the farmers to identify the right selling time. Recently, the absence of such the forecasting system makes the farmers decide to sell their commodities to middlemen which in turn, reduces their exchange rate as the length of distribution flow is complicated. The length of distribution flow is started from farmers, middlemen, wholesalers, retailers, and consumers. To address this problem, a forecasting system based on radial basis function neural network (RBFNN) is proposed. To optimize the network’s learning process, particle swarm optimization (PSO)-based learning technique is applied. The RBFNN is chosen because of its ability to generally track irregular signal changing, good speed in learning process and robustness. Meanwhile, the implementation of PSO aims to improve weight values towards global optimum in RBFNN model. |
| Databáze: | OpenAIRE |
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