基于正则化的函数连接神经网络研究及其复杂化工过程建模应用

In the modeling of chemical process, due to the high dimensionality and non-linearity of the process data, the calculation amount is greatly increased and the modeling difficulty is increased. In order to solve this problem, a regularization based functional link neural network (RFLNN) is proposed. In the proposed RFLNN method, there are two salient features through using the regularization method: on one hand, computing complexity and the amount of calculation are greatly reduced; on the other hand, the problem of local extreme values and over-fitting is effectively avoided. As a result, the performance in terms of accuracy and learning speed of functional neural network is much improved. In order to verify the effectiveness of the proposed RFLNN method, firstly, an UCI dataset called Real estate valuation is selected; then the proposed RFLNN method is used to develop a model for the complex production process of high density polyethylene (HDPE). Compared with the conventional functional link neural network(FLNN), simulation results of the selected UCI data and industrial data show that the proposed RFLNN can achieve not only fast convergence speed but also high accuracy in processing complex chemical process data.