基于特征提取的函数连接神经网络研究及其化工过程建模应用

对于化工过程中带噪声、强耦合的高维数据建模问题，常规的函数连接神经网络（functional link neural networks，FLNN）无法有效地进行处理。为解决该问题，提出一种基于主元分析（principal components analysis，PCA）的函数连接神经网络（PCA-FLNN）。通过对FLNN的函数扩展层进行特征提取，不仅去除变量间的线性相关关系，而且提取数据的主成分，进而简化FLNN学习数据的复杂度。为验证所提方法的有效性，首先采用UCI数据Airfoil Self-Noise对其性能进行验证；随后将所提的方法应用于精对苯二甲酸（purified terephthalic acid，PTA）生产过程建模；与传统FLNN进行对比，标准数据和工业数据的仿真结果表明，PCA-FLNN在处理复杂化工过程数据时具有收敛速度快和建模精度高的特点。

Research and application of feature extraction derived functional link neural network

Traditional functional link neural network (FLNN) cannot effectively model multi-dimensional, noisy and strongly coupled data in chemical process. A principal component analysis based FLNN (PCA-FLNN) model was proposed to improve modeling effectiveness. Feature extraction of FLNN function extension block not only removed linear correlations between variables but also selected main components of data, which complexity of FLNN learning data was alleviated. The proposed PCA-FLNN model was used to simulate an UCI Airfoil Self-Noise data and purified terephthalic acid (PTA) production process. Simulation results indicated that PCA-FLNN can achieve faster convergence speed with higher modeling accuracy than traditional FLNN.