A Knowledge Transfer-based Fuzzy Broad Learning System for Modeling Nonlinear Systems

Fuzzy broad learning system is regarded as an effective algorithm to utilize the measured data for modeling nonlinear systems. However, due to the possible existence of data inadequate or data loss, it is a challenge to design a suitable fuzzy broad learning system with the data shortage issue for modeling. Therefore, a knowledge transfer-based fuzzy broad learning system is developed in this paper. First, the knowledge extracted from the process is used to construct the initial condition. Then, this model can obtain the precise parameter and structure. Second, a knowledge evaluation mechanism is employed to rebuild the knowledge by judging the correlation and discrepancy. Then, the knowledge can be preferably integrated. Third, a transfer gradient algorithm is employed to adjust the parameters of fuzzy broad learning system. Then, the modeling performance of knowledge transfer-based fuzzy broad learning system can be improved. Finally, a benchmark problem and a practical application are used to test the merits of knowledge transfer-based fuzzy broad learning system. The results demonstrate that this model can achieve superior modeling performance.