Abstract In this study, a novel iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS) was proposed to predict the HHV of biomass fuels as a function of fixed carbon (FC), volatile matter (VM), and ash content. In this methodology, the PCA analysis was used to eliminate the co-linearity of experimental data for providing the required background to the INFPLS model. In the INFPLS structure, adaptive network-based fuzzy inference system (ANFIS) was applied to correlate the inputs and the outputs of iterative PLS score vectors. Furthermore, the capability of the PCA-INFPLS approach in estimating the biomass fuels HHV was compared with those of the PLS, ANFIS, NFPLS, and INFPLS models. Generally, the PCA-INFPLS approach was much more efficient than the other applied methods in modeling the biomass fuels HHV. More specifically, the developed model predicted the HHV of biomass fuels with an R2 > 0.96, an MSE

中文翻译：

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基于近似分析的生物质高热值 (HHV) 建模，使用基于迭代网络的模糊偏最小二乘法结合主成分分析 (PCA-INFPLS)

摘要 在这项研究中，提出了一种新的基于迭代网络的模糊偏最小二乘法结合主成分分析 (PCA-INFPLS) 来预测生物质燃料的 HHV 作为固定碳 (FC)、挥发性物质 (VM)、和灰分含量。在该方法中，PCA 分析用于消除实验数据的共线性，以便为 INFPLS 模型提供所需的背景。在 INFPLS 结构中，应用基于自适应网络的模糊推理系统 (ANFIS) 来关联迭代 PLS 评分向量的输入和输出。此外，PCA-INFPLS 方法在估算生物质燃料 HHV 的能力与 PLS、ANFIS、NFPLS 和 INFPLS 模型的能力进行了比较。一般来说，PCA-INFPLS 方法在模拟生物质燃料 HHV 时比其他应用方法更有效。更具体地说，开发的模型预测了生物质燃料的 HHV，R2 > 0.96，MSE