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1996年中国控制会议

作者： Ruisheng Li Institute of Systems Science Chinese Academy of Sciences Beijing, 100080, P. R. of China

＜正＞For a class of nonlinear discrete-time systems, a local convergence result that the tracking error between system output and reference command converges to a bounded ball is established by use of the neural network and the least squares （LS） algorithm with dead zone. The assumption, used in [1], that the neural network approximates the true system with accuracy ε over a compact set is not needed in this paper. No assumptions of the nonlinear functions being bounded away from zero and of the system being minimum phase are made in comparison with [1].

关键词： Nonlinear systems neural network least squares algorithm adaptive control

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The posterior selection method for hyperparameters in regularized least squares method

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Control Theory and Technology 2024年第2期22卷 184-194页

作者： Yanxin Zhang Jing Chen Yawen Mao Quanmin Zhu School of Science Jiangnan UniversityWuxi214122JiangsuChina The Science and Technology on Near-Surface Detection Laboratory Wuxi214122JiangsuChina Department of Engineering Design and Mathematics University of the West of EnglandBristolBS161QYUK

The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.

关键词： Regularization method Hyperparameter System identification least squares algorithm

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Virtual Inertia Estimation Method of DFIG-based Wind Farm with Additional Frequency Control

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1076-1087页

作者： Pengwei Chen Chenchen Qi Xin Chen Jiangsu Key Laboratory of New Energy Generation and Power Conversion College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

With the increasing penetration of wind power,using wind turbines to participate in the frequency regulation to support power system has become a clear *** accurately quantify the inertia provided by the doubly-fed induction generator(DFIG)based wind farm,the frequency response model of DFIG with additional frequency control is established,and then by using Routh approximation,the explicit expression of the virtual moment of inertia is derived for the DFIG gridconnected *** further enhance the availability of the expression,an estimation method is proposed based on the matrix pencil method and the least squares algorithm for estimating the virtual moment of inertia provided by the wind ***,numerical results tested by a DFIG grid-connected system and a modified IEEE 30-bus system verify the derived expression of the virtual moment of inertia and the proposed estimation method.

关键词： Doubly-fed induction generator(DFIG) additional frequency control virtual moment of inertia matrix pencil method least squares algorithm

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Deep Learning to Find Key Brain Electrode Positions in Emotional EEG

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Journal of Behavioral and Brain Science 2023年第12期13卷 227-242页

作者： Guoxia Zou School of Artificial Intelligence Guangdong Open University (Guangdong Polytechnic Institute) Guangzhou China

At present, the channels used for EEG acquisition are more than 16, which makes it difficult to wear EEG caps and has poor contact. Therefore, it brings difficulties to the collection of brain waves is not conducive to converting research into applications. It is a well worth studying work in researching how to find the key brain electrode in the existing brain wave, which will greatly reduce the number of EEG acquisition points during application, making it easier to translate the research into practical application. This paper takes emotional EEG as an example to study how to find the key brain electrode points of emotional EEG with deep learning method. Firstly, using the least square regression algorithm to calculate the characteristic coefficients of each electrode point;secondly, according to the law of the characteristic coefficient value, grouping the key EEG poles for experiment. In the grouping experiment, the Conv1d-GRU model used to train and verify the EEG data of the corresponding electrode points. Finally, from the results of various grouping experiments, it concluded that the selection method of the key EEG level points should be the electrode points with positive characteristic coefficient, and the accuracy of verification is 97.6%. With experiments, it confirmed that there are key electrode points in the detection of emotional EEG by 16-channel OpenBCI. There are only six key electrode points of emotional EEG;that is to say, the EEG data collected by only six key electrode points can identify seven kinds of emotional EEG. .

关键词： Electroencephalogram least squares algorithm Deep Learning Emotion Recognition

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The temperature system identification of the PVC stripper tower top based on PSO-FCM optimized T-S model

The temperature system identification of the PVC stripper to...

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第24届中国控制与决策会议

作者： Gao Shuzhi 1 , Dou Xing 1 , Gao Xianwen 2 1. College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China2. College of Information Science and Engineering, Northeastern University, Shenyang 110189, China

In view of the characteristics of T-S model, such as easily expressing complex dynamic systems and the characteristics of PSO algorithm which could find the optimal solution of complex problems easily. This paper will presents a new identification method based on the T-S model in which FCM parameters is optimized by *** mathematical model of the temperature system of the PVC stripper tower top will be built by this method . First, an adaptive number of clusters of C-means clustering fuzzy (FCM) algorithm is used to find the appropriate number of clusters in FCM, and both the number of fuzzy rules and the premise parameters of the model can are determined. Using PSO algorithm to optimize the FCM algorithm, then getting the best membership matrix by the FCM algorithm based on PSO in the ***,a least square algorithm is applied to determine the parameters of consequent part of T-S model. The simulation result shows the effectiveness and feasibility of the modeling method..

关键词： T-S model fuzzy c-means clustering (FCM) least squares algorithm polyvinyl chloride (PVC) Particle swarm optimization algorithm PSO)

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