共查询到18条相似文献,搜索用时 125 毫秒
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综合同伦方法与Levenberg-Marquardt(LM)优化方法,提出了一种新型非线性同伦LM神经网络学习算法以改善现有神经网络学习算法的学习效率,分析了不同类型的过渡函数对神经网络泛化性能的影响.该算法具有稳定性强、收敛性能好的特点.结合工业过程实际要求,将提出的改进算法用于丙烯腈收率神经网络软测量建模并与几种常见建模方法比较,结果表明:基于改进算法的软测量模型具有更高的测量精度和更好的泛化性能,满足现场测量要求. 相似文献
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在分析基本微粒群优化算法(PSO)和支持向量机(SVM)原理的基础上,采用带有末位淘汰机制的微粒群优化算法优化支持向量机的参数,建立了延迟焦化装置粗汽油干点软测量的微粒群支持向量机模型.该方法利用支持向量机结构风险最小化原则和PSO算法快速全局优化的特点,用于软测量建模.仿真实验表明:所建模型的泛化性能较好,模型具有较高的精度. 相似文献
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提出一种改进算法,用来解决现有最小二乘支持向量机方法在处理大规模样本软测量建模问题时出现的模型结构复杂、失去支持向量稀疏性且正规化参数和核参数难以确定等问题。对样本集进行预处理,通过计算样本间欧氏距离进行样本相似程度分析,去除样本集中1/3的样本以简化支持向量机模型结构并提高计算速度。定义了一种混沌映射构成混沌系统并分析了其遍历性。应用改进的混沌优化算法优化最小二乘支持向量机模型参数以提高模型的拟合精度和泛化能力。将改进算法用于丙烯腈收率软测量建模中,仿真实验结果表明:模型精度较高,泛化性能好,满足现场测量要求。 相似文献
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提出了一种通过调整减法聚类半径优选模糊规则的软测量建模方法。首先用减法聚类建立T—S模糊模型,然后通过调整聚类半径优选模糊规则数,以取得具有良好泛化性能的模型,之后利用梯度下降混合最小二乘算法精调参数。最后用该方法对初馏塔石脑油干点进行软测量建模,结果表明能较快确定优化模型,并能满足软测量建模精度要求。 相似文献
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提出了一种基于高斯过程(GP)和偏最小二乘法(PLS)的非线性PLS方法(GP-PLS),以更加有效地处理过程非线性、多输入和数据共线性等复杂特性,提高模型的推广能力和精度。该方法首先采用PLS进行特征提取,再用GP建立PLS的内部模型,因而具有GP与PLS的优点。对工业丙烯腈生产过程丙烯腈收率软测量建模的应用表明,采用该方法建立的软测量模型在模型精度、推广能力等方面明显优于一些传统软测量建模方法,满足工业现场应用要求。 相似文献
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介绍了核函数方法的基本原理及两种核函数统计建模方法;提出了用核函数PLS与核函数PCR建立工业丙烯腈生产过程丙烯腈收率软测量模型,以便更有效地处理过程非线性、多输入和数据共线性等复杂特性。对比研究发现,基于核函数方法的软测量模型要优于线性统计模型,而核函数PLS模型性能优于核函数PCR。 相似文献
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针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象,提出一种改进的带扰动项粒子群算法并进行收敛性分析。算法中引入进化速度因子,当粒子进化速度低于一定值时在粒子速度更新方程中添加扰动项使粒子逃离局部最优区而继续搜索。对几个复杂函数的寻优测试表明:改进算法的收敛速度、收敛精度和全局搜索性能均有显著提高。将本方法用于建立丙烯腈收率神经网络软测量建模,研究结果表明模型精度较高、泛化性能好,满足现场测量要求。 相似文献
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提出了一种基于GA s/PSO组合算法的P ID控制器参数自整定方法,这种方法兼有遗传算法(GA s)和粒子群算法(PSO)的优点。组合算法种群由GA s和PSO的最佳个体迁移形成,其中GA s采用了实数编码和变异概率自适应,PSO算法采用了带指数衰减的惯性因子的速度更新算法,以加快收敛速度。通过对水轮机调速系统P ID控制器参数寻优仿真比较表明,该组合算法寻优性能比单独的GA s和PSO表现更为优异,且所得系统具有更好的动态性能。 相似文献
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Yi Lan LIAO Jin Feng WANG Ji Lei WU Jiao Jiao WANG Xiao Ying ZHENG 《Biomedical and environmental sciences : BES》2012,25(5):569-576
ObjectiveTo develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world.MethodsThe region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects (NTDs). A hybrid particle swarm optimization/ant colony optimization (PSO/ACO) algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes.ResultsThe algorithm was easy to apply, with the accuracy of the results being 69.5%±7.02% at the 95% confidence level.ConclusionThe proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations. 相似文献
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Clinical pathways’ variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating
diseases or even endanger patients’ life if not handled effectively. In order to improve the accuracy and efficiency of variances
handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs)
is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and
parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimization algorithm
(RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative
chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm
achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and
T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs. 相似文献
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Although the clinical pathway (CP) predefines predictable standardized care process for a particular diagnosis or procedure,
many variances may still unavoidably occur. Some key index parameters have strong relationship with variances handling measures
of CP. In real world, these problems are highly nonlinear in nature so that it’s hard to develop a comprehensive mathematic
model. In this paper, a rule extraction approach based on combing hybrid genetic double multi-group cooperative particle swarm
optimization algorithm (PSO) and discrete PSO algorithm (named HGDMCPSO/DPSO) is developed to discovery the previously unknown
and potentially complicated nonlinear relationship between key parameters and variances handling measures of CP. Then these
extracted rules can provide abnormal variances handling warning for medical professionals. Three numerical experiments on
Iris of UCI data sets, Wisconsin breast cancer data sets and CP variances data sets of osteosarcoma preoperative chemotherapy
are used to validate the proposed method. When compared with the previous researches, the proposed rule extraction algorithm
can obtain the high prediction accuracy, less computing time, more stability and easily comprehended by users, thus it is
an effective knowledge extraction tool for CP variances handling. 相似文献
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