2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第10页

A Novel System Decomposition Method Based on Pearson Correlation and Graph Theory* 一种基于Pearson相关和图论的系统分解新方法*

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515967

Jing Jin, Shu Zhang, L. Li, T. Zou

{"title":"A Novel System Decomposition Method Based on Pearson Correlation and Graph Theory*","authors":"Jing Jin, Shu Zhang, L. Li, T. Zou","doi":"10.1109/DDCLS.2018.8515967","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515967","url":null,"abstract":"With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In the traditional system decomposition methods based on graph theory, the weight on each edge of the graph is set by state space equation to reflect the mutual influence of variables in the system. But in the actual industrial process, the acquisition of state space equation is more difficult. In this paper, a system decomposition method based on Pearson correlation coefficient and graph theory is proposed to avoid the use of state space equations. At first, a directed graph is established to represent the actual process of the industrial system and the weights on corresponding edges in the directed graph are set by the Pearson correlation coefficients between two nodes connected by these edges. Then the directed graph is decomposed into several initial subgraphs and the subgraphs will be fused according to a certain rule. Here, a fusion index is defined to select the optimal fusion results in each fusion process. After each fusion process, the termination condition is required to determine whether to continue the next round of fusion process. When the fusion process ends, the subsets obtained at this time are the results of the system decomposition. When the system decomposition is finished, the online subsystems modeling will be carried out by RPLS algorithm. Finally, the proposed algorithm is applied in the Tennessee Eastman process to verify the validity.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"819-824"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86112311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Convergence Performance of Discrete Power Attracting Law 离散功率吸引律的收敛性能

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515952

Lingwei Wu, Mingxuan Sun, Guang Chen

引用次数: 2

A State of Charge Estimation Approach Based on Fractional Order Adaptive Extended Kalman Filter for Lithium-ion Batteries 基于分数阶自适应扩展卡尔曼滤波的锂离子电池充电状态估计方法

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516091

Mengen Xu, Qiao Zhu, Meng’qian Zheng

引用次数: 6

Sampled-data Control for T-S Fuzzy Systems with Quantized Signals 带有量化信号的T-S模糊系统的采样数据控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516042

Xiaojing Han, Ningwei Cheng, Yuechao Ma

引用次数: 0

Fixed-Time Stabilization for Interconnected Systems with Discontinuous Interconnections and Nonidentical Perturbations 具有不连续互联和非同摄动的互联系统的定时镇定

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516115

Nannan Rong, Zhanshan Wang, Huaguang Zhang

引用次数: 0

Design optimization of Permanent Magnet Brushless Direct Current Motor using Radial Basis Function Neural Network 基于径向基函数神经网络的永磁无刷直流电机优化设计

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515983

Darong Sorn, Yong Chen

引用次数: 3

Online Semi-supervised Quality Prediction Model for Batch Mixing Process 间歇混合过程的在线半监督质量预测模型

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516014

Mingtao Zhang, Bocheng Chen, You Wu, Wei-wei Deng, Xuelei Zhang, Yi Liu

引用次数: 0

An Iterative Learning Controller for Superheat Degree of VCC System VCC系统过热度的迭代学习控制器

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516037

Xiaohong Yin, Xinli Wang, Ximei Liu, R. Chi, Mingming Lin, Fanglin Liu

引用次数: 0

Ensemble of Extreme Learning Machines for Regression 回归的极限学习机集合

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515915

Atmane Khellal, Hongbin Ma, Qing Fei

{"title":"Ensemble of Extreme Learning Machines for Regression","authors":"Atmane Khellal, Hongbin Ma, Qing Fei","doi":"10.1109/DDCLS.2018.8515915","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515915","url":null,"abstract":"Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme Learning Machines (ELMs) for regression. The developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett’s theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. The experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyperparameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"26 1","pages":"1052-1057"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87667930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Data-driven Adaptive Iterative Learning Control Based on a Local Dynamic Linearization 基于局部动态线性化的数据驱动自适应迭代学习控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516008

Shuhua Zhang, Yu Hui, R. Chi

引用次数: 4