通过使用基于Swarm-Intelligent的优化算法来训练Feed-Forward神经网络,以实现最大功率点跟踪
Ebubekir Kaya1, Ceren Baştemur Kaya2, Emre Bendeş1
1Department of Computer Engineering, Engineering Architecture Faculty, Nevsehir Haci Bektas Veli University, Nevşehir 50300, Türkiye.
Biomimetics (Basel, Switzerland)
|September 27, 2023
概括
评估了13个群体智能算法,用于训练人工神经网络,以追踪最大功率点. 火算法,自私群优化器和虫优化算法在训练和测试中表现出卓越的性能.
科学领域:
- 人工智能的人工智能
- 可再生能源系统可再生能源系统
- 优化算法 优化算法
背景情况:
- 人工神经网络 (ANN) 对于可再生能源的最大功率点跟踪 (MPPT) 是至关重要的.
- 有效的MPPT在很大程度上依赖于ANN的成功培训.
- 听算法,特别是群体智能,被广泛用于ANN培训.
研究的目的:
- 评估和排名13个集群智能优化算法用于训练前神经网络.
- 确定最有效的算法,以实现准确的最大功率点跟踪.
- 使用平均平方误差在不同神经网络结构中评估算法性能.
主要方法:
- 利用了13个群体智能算法:人工蜂群,蝶优化,子搜索,群体优化,龙算法,火虫算法,虫优化算法,鸟群算法,粒子群体优化,鱼群算法,自私群体优化器,衣群算法和金枪鱼群优化.
- 使用这些算法训练了MPPT的前神经网络.
- 根据培训和测试阶段的平均平方误差 (MSE) 评估的性能.
主要成果:
- 火算法,自私的群体优化器和虫优化算法被列为前三大最成功的算法.
- 实现低训练和测试平均平方误差:4.5 × 10-4 ,1.6 × 10-3和2.3 × 10-3分别用于训练和4.6 × 10-4 ,1.6 × 10-3和2.4 × 10-3分别用于测试.
- 在较少的评估数量下获得了有效的结果,这表明了效率.
结论:
- 火算法,自私群优化器和虫优化算法对MPPT的ANN培训非常有效.
- 评估的群集智能算法通常为MPPT应用提供可接受的结果.
- 这些算法显示出在可再生能源系统中提高MPPT效率的巨大潜力.
相关概念视频
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