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Advanced Control for Applications: engineering and Industrial Systems 2020年第2期2卷

作者： Guo, Jingjing Zhang, Pengpeng Liu, Tengfei Jiang, Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Electrical and Computer Engineering New York University BrooklynNY United States

This article studies the event-triggered stabilization problem for a class of nonlinear systems subject to time delays and external disturbances. A new event trigger is developed by introducing an auxiliary variable to estimate the influence of external disturbances, time delays and the unmeasured states, such that infinitely fast sampling is avoided. The time-delay version of the nonlinear small-gain theorem is employed to guarantee the stability of the closed-loop event-triggered system. The new design is applied to solving the event-triggered output-feedback control problem for a class of nonlinear uncertain systems in the output-feedback form. © 2020 John Wiley & Sons, Ltd.

关键词： Time delay

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Data driven particle size estimation of hematite grinding process using stochastic configuration network with robust technique

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Journal of Central South University 2019年第1期26卷 43-62页

作者： DAI Wei LI De-peng CHEN Qi-xin CHAI Tian-you School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares ***,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty *** experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and *** results show that the RSCN has an excellent performance for the PS estimation.

关键词： hematite grinding process particle size stochastic configuration network robust technique M-estimation nonparametric kernel density estimation

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Operating Performance Assessment Based on Stacked Sparse Denoising Auto-encoder for Gold Hydrometallurgy processes

Operating Performance Assessment Based on Stacked Sparse Den...

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Chinese Control and Decision Conference, CCDC

作者： Kaiqing Bu Fuli Wang Yan Liu College of Information Science & Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The process operating performance assessment (POPA) is critical for industrial processes to pursue optimal comprehensive economic benefit. In this article, a two-stage deep unsupervised feature learning approach for the industrial POPA is proposed. In the first stage, we utilize stacked sparse denoising auto-encoder (SSDA) to extract deep features from raw input data with noise of different performance grades, which can overcome drawbacks of traditional approach and automatically extract features from process variable correlation characteristics. In the second stage, softmax regression is employed to train a neural network classifier for the features of different performance grades. The presented method is illustrated by a gold hydrometallurgy process. The simulation results show that the proposed SS-DA method obtains fairly high assessment accuracy and strong robustness than the total projection to latent structures (T-PLS) method even under strong noise interference environment.

关键词： Feature extraction Noise reduction Training Gold Data mining Economics Neural networks

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An Improved Self-Organizing Maps Neural Network Model for Plantar Pressure Image Classification

An Improved Self-Organizing Maps Neural Network Model for Pl...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zairan Li Dan Wang Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University PR China Wenzhou Polytechnic Wenzhou PR China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

A Self-organizing map neural network combining with classification output layers network was proposed for classifying plantar pressure dataset, which was acquired from foot 3D scan system after features extraction. The single SOM with 10, 15, 20, 30 hidden layers was compared and validated. Subsequently, the classification modeling with SOM combining multiple hidden neural networks was proposed, and results showed that the proposed model performs high effectiveness in plantar pressure dataset and has potential applications on comfort design through last customization.

关键词： Self-organizing feature maps Training Three-dimensional displays Neural networks Manuals Information technology Unsupervised learning

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Zeroth-order algorithms for stochastic distributed nonconvex optimization

arXiv

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arXiv 2021年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being distributed over n agents having access only to zeroth-order (ZO) information of the cost. This problem has various machine learning applications. As a solution, we propose two distributed ZO algorithms, in which at each iteration each agent samples the local stochastic ZO oracle at two points with a time-varying smoothing parameter. We show that the proposed algorithms achieve the linear speedup convergence rate O(√p=(nT)) for smooth cost functions under the state-dependent variance assumptions which are more general than the commonly used bounded variance and Lipschitz assumptions, and O(p=(nT)) convergence rate when the global cost function additionally satisfies the Polyak-Lojasiewicz (P-L) condition in addition, where p and T are the dimension of the decision variable and the total number of iterations, respectively. To the best of our knowledge, this is the first linear speedup result for distributed ZO algorithms, which enables systematic processing performance improvements by adding more agents. We also show that the proposed algorithms converge linearly under the relative bounded second moment assumptions and the P-L condition. We demonstrate through numerical experiments the efficiency of our algorithms on generating adversarial examples from deep neural networks in comparison with baseline and recently proposed centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Cost functions

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Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics

arXiv

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arXiv 2022年

作者： Dai, Wei Ning, Chuanfeng Pei, Shiyu Zhu, Song Wang, Xuesong The School of Information and Control Engineering Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The School of Mathematics China University of Mining and Technology Xuzhou221116 China

As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In the light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram-Schmidt orthogonalization technology. The universal approximation property of OSCN and an adaptive setting for the key construction parameters have been presented in details. In addition, an incremental updating scheme is developed to dynamically determine the output weights, contributing to improved computational efficiency. Finally, experimental results on two numerical examples and several real-world regression and classification datasets substantiate the effectiveness and feasibility of the proposed approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Estimation of total heat exchange factor based on adaptive kernel density estimation theory

Estimation of total heat exchange factor based on adaptive k...

引用

Chinese Control and Decision Conference, CCDC

作者： Xiaochuan Luo Mingwei Jiang Lu Yu Yuhao Fan Lei Zhang College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Total heat exchange factor plays an important role in determining the ideal temperature rise curve of the slab under steady-state conditions. The "Black Box" experiment and the furnace temperature data measured by the thermocouples in the reheating furnace contain uncertainties and big noise that will affect the identification accuracy. In order to solve the above problems, this paper proposes weighted least squares method (WLS) based on adaptive kernel density theory. In the calculation process, WLS can adaptively reduce the influence of noise on the identification results, and avoid the "residual pollution" and "residual flooding" that may occur in the traditional fixed bandwidth kernel density estimation method. In this paper, a numerical algorithm combining conjugate gradient method (CGM) and gradient projection method (GPM) is used to iteratively solve the WLS model, which ensures the stability of the iterative process and improves the identification accuracy. Finally, the CuPCrNi experimental slab data was used to verify the effectiveness of the adaptive kernel density estimation method and ensure robustness.

关键词： Temperature measurement Kernel Furnaces Adaptation models Heating systems Mathematical model Estimation

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Fully Distributed Adaptive Event-Triggered Leaderless Consensus Protocol for Physically Interconnected Multi-agent Systems

Fully Distributed Adaptive Event-Triggered Leaderless Consen...

引用

Chinese Control and Decision Conference, CCDC

作者： Weihua Li Huaguang Zhang Yingchun Wang Chong Liu College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) Shenyang China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In this study, the leaderless consensus issue is considered for general linear multi-agent systems with physically interconnected network under an undirected connected graph. Based on the local states information feedback, a novel fully distributed controller that uses adaptive event-triggered protocol is developed, which can ensure that every agent achieve consensus without requiring continuous communication and global network information. Meanwhile, it is proved that the Zeno behavior isn't exhibited by demonstrating the lower-bound of minimum inter-event interval is strictly positive under the proposed control protocol. Compared to the existing related papers, the salient feature of this paper is that both physically interconnected network and communication interconnected network of the multi-agent systems are considered on the premise that the designed event-triggered consensus protocol are fully distributed, asynchronous. Therefore, this makes the systems are more comprehensive and practical. Finally, the performance and effectiveness of the protocol are displayed by a example.

关键词： Protocols Multi-agent systems Artificial neural networks Adaptive control Graph theory Industries

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Global Disturbance Rejection of Switched Nonlinear Systems with Switched Internal Model

引用

Journal of Systems Science & Complexity 2018年第1期31卷 161-172页

作者： LONG Lijun College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This paper investigates the problem of global disturbance rejection for a class of switched nonlinear systems where the solvability of the disturbance rejection problem for subsystems is not assumed. The disturbances are assumed to be sinusoidal with completely unknown frequencies, phases and amplitudes. First, as an extension of the classic concept of internal model for non-switched systems, a switched internal model is proposed. Second, in order to solve the problem under study, an adaptive control method is established on the basis of the multiple Lyapunov functions method. Also,adaptive state-feedback controllers of subsystems are designed and incorporated with a switching law to asymptotically reject the unknown disturbances. Finally, an example is provided to demonstrate the effectiveness of the proposed design method.

关键词： Disturbance rejection multiple Lyapunov functions switched internal model switched nonlinear systems

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An Energy Consumption Prediction LSTM Model of Metallurgy Enterprises

An Energy Consumption Prediction LSTM Model of Metallurgy En...

引用

作者： Xueying Wang Zhuchao Yu Pengfei Xi Gaixia Chu Shiyang Lai Jing Li Yuanzheng Zhang School of Business Administration Northeastern University State Key Laboratory of Process Automation in Mining & Metallurgy College of Medicine and Biological Information Engineering Northeastern University

A iming at the characteristics of mu lti-dimensional production data,comp licated sources and diverse data structures of metallurgy enterprises,it is of great significance to study how to use energy management-related data to predict metallurgy enterprises' energy *** with an accurate measurement method,the enterprises can not only reduce the cost of energy consumption but also develop economic efficiency in producing for metallurgy enterprises,which results in low energy use efficiency,high enterprise cost,and weak *** th is paper,we establish an LSTM model to achieve energy consumption prediction for metallurgy enterprises by optimizing the model's parameters using a grid search algorith *** also compare our model's pred iction results with other mainstream machine learning algorith ms,i.e.,MARS and SVM through indexes such as MSE,RMSE,MAPE,and MRA E to evaluate the predict ion effect of the learning algorith *** to our simulat ion,LSTM performs best in the task of energy consumption prediction.

关键词： MSE An Energy Consumption Prediction LSTM Model of Metallurgy Enterprises Model

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