摘要
针对置信规则库(BRB)中参数优化模型的求解问题,引入群智能算法中的粒子群优化(PSO)算法,提出一种新的参数训练方法。将参数优化模型求解问题转换为带约束条件的非线性优化问题,在迭代寻优时限制粒子在搜索空间中,对失去速度的粒子重新赋予速度,维持种群中粒子多样性,从而实现参数训练。在输油管道检漏问题仿真实验中,训练后系统的平均绝对误差(MAE)为0.166478。实验结果表明,所提方法有理想的收敛精度,可用于置信规则库参数训练。
To solve the problem of optimization learning models in Belief Rule Base (BRB), a new parameter training approach based on the Particle Swarm Optimization (PSO) algorithm was proposed, which is one of the swarm intelligence algorithms. The optimization learning model was converted to nonlinear optimization problem with constraints. During the optimization process, all particles were limited in the search space and the particles with no speed were given velocity in order to maintain the diversity of the population of particles and achieve parameter training. In the practical pipeline leak detection problem, the Mean Absolute Error (MAE) of the trained system was 0.166478. The experimental results show the proposed method has good accuracy and it can be used for parameter training.
出处
《计算机应用》
CSCD
北大核心
2014年第8期2161-2165,2174,共6页
journal of Computer Applications
基金
国家自然科学基金青年项目(61300026
61300104)
国家自然科学基金面上项目(71371053)
国家杰出青年科学基金资助项目(70925004)
福建省教育厅A类科技项目(JA13036)
福州大学科技发展基金资助项目(2014-XQ-26)
关键词
置信规则库
证据推理
粒子群优化算法
参数优化模型
参数训练
Belief Rule Base (BRB)
Evidential Reasoning (ER)
Particle Swarm Optimization (PSO) algorithm
parameter optimization model
parameter training