计算机科学与探索Issue(8)：989-1001,13.DOI:10.3778/j.issn.1673-9418.1403061
基于加速梯度求法的置信规则库参数训练方法
Parameter Training Approach for Belief Rule Base Using the Accelerating of Gra-dient Algorithm
摘要
Abstract
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.关键词
置信规则库(BRB)/参数训练/非线性优化问题/加速梯度求法Key words
belief rule base (BRB)/parameter training/nonlinear optimization problem/the accelerating of gradient algorithm分类
信息技术与安全科学引用本文复制引用
吴伟昆,杨隆浩,傅仰耿,张立群,巩晓婷..基于加速梯度求法的置信规则库参数训练方法[J].计算机科学与探索,2014,(8):989-1001,13.基金项目
The National Natural Science Foundation of China under Grant Nos.71371053,61300026,61300104(国家自然科学基金) （国家自然科学基金）
the Science and Technology Project of Fujian Education Department under Grant No. JA13036(福建省教育厅科技项目) （福建省教育厅科技项目）
the National Collegiate Innovation and Entrepreneurship Training Program of China under Grant No.121038607(国家级大学生创新创业训练计划项目) （国家级大学生创新创业训练计划项目）
the Science and Technology Development Foundation of Fuzhou University under Grant No.2014-XQ-26(福州大学科技发展基金项目) （福州大学科技发展基金项目）
