使用修改过渡搜索优化算法开发的squeezenet进行PEMFC模型识别
Rulin Duan1, Defeng Lin2, Gholamreza Fathi3
1School of Computing, Guangdong Vocational Institute of Public Administration, Guangzhou, 510800, Guangdong, China.
Heliyon
|March 28, 2024
概括
本研究介绍了一种改进的深度学习模型和优化算法,用于准确的质子交换膜燃料电池 (PEMFC) 建模. 新方法提高了清洁能源应用的效率和准确性.
科学领域:
- 能源系统工程 能源系统工程
- 计算科学 计算科学
- 可再生能源技术可再生能源技术
背景情况:
- 质子交换膜燃料电池 (PEMFC) 对于清洁能源至关重要,但准确的系统建模对于性能和效率至关重要.
- 现有的PEMFC建模技术面临的挑战包括缓慢的融合,高的计算需求和不够的准确性.
- 有效的建模对于PEMFC系统的最佳设计,控制和监控至关重要.
研究的目的:
- 为准确的PEMFC建模和参数识别开发一种增强的方法.
- 克服现有的PEMFC建模方法的局限性.
- 提高PEMFC系统分析的效率和准确性.
主要方法:
- 使用修改后的SqueezeNet深度学习模型来降低计算复杂性.
- 为了增强搜索功能,引入了一个新的修改过渡搜索优化 (MTSO) 算法.
- 结合方法用于在各种操作条件下建模PEMFC输出电压.
主要成果:
- 拟议的方法在与封闭的反复单元/改进的曼塔射线食优化 (GRU/IMRFO) 和灰色神经网络模型/粒子群优化 (GNNM/PSO) 相比显示出更高的性能.
- 该方法实现了最小的平方误差和 (SSE),表明PEMFC电压建模的高精度.
- 经验数据验证了开发的建模技术的有效性和优越性.
结论:
- 增强的PEMFC建模方法比现有方法提供了更好的准确性和效率.
- 这项研究有助于更好地设计,控制和监测PEMFC系统.
- 这些发现有助于推动清洁和可再生能源技术的发展.
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