Data-Knowledge-Based Fuzzy Neural Network for Nonlinear System Identification

Abstract

Many nonlinear dynamical systems are usually lack of abundant datasets since the data acquiring process is time consuming. It is difficult to utilize the incomplete datasets to build an effective data-driven model to improve the industry productivity. To overcome this problem, a data-knowledge-based fuzzy neural network (DK-FNN) was developed in this article. Compared with the existing methods, the proposed DK-FNN consists of the following obvious advantages. First, through the multilayered connectionist structure, this proposed DK-FNN could not only make full use of the data from the current scene, but also use the existing knowledge from the source scene to improve the learning performance. Second, an integrated-form transfer learning (ITL) method was developed to improve the learning performance of DK-FNN. This first reported ITL method was able to integrate the internal information from the datasets in the source scene and the knowledge from the current scene to offset the data shortage in the learning process. Third, a mutual attraction strategy (MAS) was designed to balance the difference of data distributions to reduce the identification errors of DK-FNN. Then, the proposed DK-FNN was able to satisfy the nonlinear dynamical systems. Finally, the effectiveness and the merit of DK-FNN were validated by applying it to several practical systems.