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.