摘要
在铝电解槽非稳态情况下,槽参数易发生局部突变,呈现非高斯概率分布,且各种槽参数相关性较强,无法满足概率神经网络中训练样本必须服从独立同分布的假设条件,影响槽况诊断的精确度。提出一种基于对称Alpha稳定分布概率神经网络的铝电解槽况诊断方法,利用其对非高斯分布数据的良好近似拟合能力,改进模式层的径向基函数,提高概率神经网络对槽参数局部突变的适应性。通过取自某厂170kA大型预焙槽的样本进行检验表明,该方法能够对5种槽况做出正确的诊断,具有较强的分类精度和收敛速度。
The numerous variables in non-steady state of aluminum reduction cells are non-Gaussian and impulsive.Due to correlation of variables,the condition that training samples must be independent and identically distributed is not fulfilled.For these reasons,it is too hard to diagnose the status of aluminum reduction cells in application.A diagnosis method for the status of aluminum reduction cells based on symmetric Alpha-stable（SαS）probabilistic distribution neural network was proposed.In the method,probability density function of SαS was introduced as radial basis function of model layer into the probabilistic neural network because such function had good fitting ability to non-Gaussian distributed data.And it also improved neural network approximation ability of partial pulse burst.By using 40 groups data of 170 kA operating aluminum smelter from a factory,this method could diagnose five statuses of aluminum reduction cells correctly and had not only stronger adaptability and robustness,but also approximation reliability and fast convergence.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2012年第10期3196-3201,共6页
CIESC Journal
基金
国家自然科学基金项目(61174015)
重庆市自然科学基金重点项目(CSTC2012JJB40006)
重庆市教委科学技术研究项目(KJ121410)
重庆科技学院校内科研基金项目(CK2011B04)~~
关键词
对称Alpha稳定分布
概率神经网络
故障诊断
铝电解槽
概率密度函数
symmetric Alpha-stable distribution
probabilistic neural network
fault diagnosis
aluminum reduction cell
probability density function
