Parameter training approach based on variable particle swarm optimization for belief rule base

doi:10.11772/j.issn.1001-9081.2014.08.2161

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (8): 2161-2165.DOI: 10.11772/j.issn.1001-9081.2014.08.2161

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Parameter training approach based on variable particle swarm optimization for belief rule base

SU Qun¹,YANG Longjie¹,FU Yanggeng¹,WU Yingjie¹,GONG Xiaoting²

1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou Fujian 350116, China;
2. College of Economics and Management, Fuzhou University, Fuzhou Fujian 350116, China

Received:2014-04-29 Revised:2014-05-08 Online:2014-08-10 Published:2014-08-01
Contact: GONG Xiaoting

基于变速粒子群优化的置信规则库参数训练方法

苏群¹,杨隆浩¹,傅仰耿¹,吴英杰¹,巩晓婷²

1. 福州大学数学与计算机科学学院，福州350116
2. 福州大学经济与管理学院，福州350116

通讯作者: 巩晓婷
作者简介:苏群(1991-),男,福建宁德人,硕士研究生,主要研究方向：智能决策、置信规则库推理；杨隆浩(1990-),男,福建南平人,硕士研究生,主要研究方向：智能决策、置信规则库推理；傅仰耿(1981-),男,福建泉州人,讲师,博士,主要研究方向：不确定多准则决策、置信规则库推理、移动互联网；吴英杰(1979-),男,福建泉州人,副教授,博士,主要研究方向：数据挖掘、数据安全与隐私保护；巩晓婷(1982-),女,河南漯河人,讲师,硕士,主要研究方向：不确定多准则决策、信息隐藏。
基金资助:
国家自然科学基金青年项目;国家杰出青年科学基金项目;国家自然科学基金面上项目;福建省教育厅A类科技项目;福州大学科技发展基金资助项目

Abstract

Abstract:

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.

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。

CLC Number:

TP18
TP273.5

SU Qun YANG Longjie FU Yanggeng WU Yingjie GONG Xiaoting. Parameter training approach based on variable particle swarm optimization for belief rule base[J]. Journal of Computer Applications, 2014, 34(8): 2161-2165.

苏群杨隆浩傅仰耿吴英杰巩晓婷. 基于变速粒子群优化的置信规则库参数训练方法[J]. 计算机应用, 2014, 34(8): 2161-2165.

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Parameter training approach based on variable particle swarm optimization for belief rule base

基于变速粒子群优化的置信规则库参数训练方法

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