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首页> 外文期刊>Journal of ambient intelligence and humanized computing >An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement
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An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement

机译：一种基于信念规则和粒子群算法的高效软计算技术，用于水松纸渗透性测量

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摘要

This paper proposes a soft computing technology based on belief rule base (BRB) system for the tipping paper permeability measurement in tobacco factory. In current studies about BRB, both the referential values of the antecedent attributes and the utilities of the consequents are given in advance and are not trained by using the dedicated optimal algorithms. The limitations of expert knowledge may lead to error of BRB because both the referential values and the utilities make a real difference on the structure of BRB, and the appropriate structure is helpful for tuning parameters more accurately. Therefore, this paper focuses on the structure and parameters optimization of BRB (SPO-BRB) by taking the referential values of the antecedent attributes and the utilities of the consequents into account to improve the input-output modeling ability of BRB. However, SPO-BRB is a nonlinear nonconvex optimization problem (NNOP). To deal with the NNOP of SPO-BRB, a particle swarm optimization algorithm with improved velocity update way and repair methods (PSO_VR) is proposed. A case study based on the data collected from a tobacco factory of china is carried out. The test results demonstrate the functionality of SPO-BRB and the effectiveness of PSO_VR.
机译：提出了一种基于信念规则库（BRB）系统的软计算技术，用于烟厂的水松纸渗透性测量。在当前有关BRB的研究中，先行属性的参考值和结果的效用都是预先给出的，并且未使用专用的最佳算法进行训练。专家知识的局限性可能会导致BRB的错误，因为参考值和实用程序都对BRB的结构产生了实际的影响，并且适当的结构有助于更精确地调整参数。因此，本文着眼于BRB（SPO-BRB）的结构和参数优化，通过考虑前项属性的参考值和结果的效用来提高BRB的输入输出建模能力。但是，SPO-BRB是一个非线性非凸优化问题（NNOP）。针对SPO-BRB的NNOP问题，提出了一种具有改进的速度更新方式和修复方法的粒子群优化算法（PSO_VR）。基于从中国一家烟草厂收集的数据进行了案例研究。测试结果证明了SPO-BRB的功能和PSO_VR的有效性。

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