论文标题
对工业加热通气和空调控制系统的单一和混合神经模糊模型的比较分析
Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System
论文作者
论文摘要
机器学习方法与软计算技术的杂交是提高预测模型性能的必要方法。尤其是混合机器学习模型，在高性能控制系统的发展方面已广受欢迎。在供暖，通风和空调（HVAC）系统的控制电路中使用的Exergy破坏和能源消耗的预测模型的较高准确性和更好的性能在工业规模上可以非常经济，以节省能源。这项研究提出了两个自适应神经模糊的推理系统粒子群优化（ANFIS-PSO）的混合模型，以及用于HVAC的自适应神经模糊的推理系统基因基因算法（ANFIS-GA）。将结果与单个ANFIS模型进行比较。 RMSE为0.0065的ANFIS-PSO模型，MAE为0.0028，R2等于0.9999，最小偏差为0.0691（kj/s），优于ANFIS-GA和单个ANFIS模型。
Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.
