The problem of training parameters for belief rule base (BRB) is essentially a nonlinear optimization problem with constraints, which is mainly solved by the FMINCON function or the swarm intelligence algorithms. However, these approaches have many shortages, such as poor portability, difficult to be implemented and requiring a large amount of calculation. To solve these problems, this paper proposes a new parameter training approach for BRB using the accelerating of gradient algorithm, which is improved from the existing parameter training methods, and is applied to the parameter training of multimodal function and pipeline leak detection. The proposed approach is compared with other traditional approaches in terms of convergence error, convergence precision and Pearson correlation coefficient in experiment analysis. The results show the better comprehensive benefits of the proposed approach, including convergence accuracy and convergence speed.
WU Weikun,YANG Longhao,FU Yanggeng et al. Parameter Training Approach for Belief Rule Base Using the Accelerating of Gradient Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2014, 8(8): 989-1001.