Performance Prediction Model of Solid Oxide Fuel Cell System Based on Neural Network Autoregressive with External Input Method
| Autor: | Shan-Jen Cheng, Jing-Kai Lin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Correctness
Computer science 020209 energy Taguchi orthogonal array Bioengineering 02 engineering and technology Overfitting lcsh:Chemical technology lcsh:Chemistry Control theory 0202 electrical engineering electronic engineering information engineering Performance prediction Chemical Engineering (miscellaneous) lcsh:TP1-1185 Input method SOFC NNARX model Series (mathematics) Artificial neural network Process Chemistry and Technology 021001 nanoscience & nanotechnology Autoregressive model lcsh:QD1-999 multi-step prediction 0210 nano-technology Realization (probability) |
| Zdroj: | Processes Volume 8 Issue 7 Processes, Vol 8, Iss 828, p 828 (2020) |
| ISSN: | 2227-9717 |
| DOI: | 10.3390/pr8070828 |
| Popis: | An accurate performance prediction model for the solid oxide fuel cell (SOFC) system not only contributes to the realization of the operating condition but also plays a role in long-term prediction performance. Accordingly, a research study has been developed to suitably deal with the time-series model and accurately build the performance prediction model of SOFC system based on neural network autoregressive with external input (NNARX) method. The architecture regressor parameters of the NNARX model were efficiently determined using the Taguchi orthogonal array (OA) method for optimal sets. The identified and evaluated optimal parameter levels were used to conduct an analysis of variance (ANOVA) to prove correctness. Moreover, a series of statistics criteria and multi-step prediction were also employed for investigating the uncertainty of the predicted model and solve the overfitting and under fitting problems further. These criteria were also used to determine the performance of the proposed model architecture. The predicted results of the current study indicated that the developed optimal model level parameters consistently had the least statistics errors and reduced workload of the trial-and-error processes. |
| Databáze: | OpenAIRE |
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