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35th Chinese Control and Decision Conference (CCDC)

作者： Cao Yu Teng Shungao Liu Jingmin Yunnan Vocat Coll Iand & Resources Kunming Yunnan Peoples R China Yunnan Copper Stock Southwest Copper Branch Kunming Yunnan Peoples R China

ISBN: (纸本)9798350334722

Based on the understanding of the changing characteristics of working conditions in the casting process of copper anode plate, on the basis of establishing a good mathematical model, the smoothness control of copper water flow rate in the casting process and the fallback control of the casting package are completed by using a self-learning algorithm, so as to realize the accurate control of the weight of the copper anode plate. After a period of debugging and trial operation in a copper company, the correctness and adaptability of the control algorithm were verified.

关键词： Copper anode plate Pull-back control self-learning algorithm Mathematical model

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Proposing a new control method for active stabilizer bars using an intelligent self-learning algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Nguyen, Tuan Anh Thuyloi Univ 175 Tay Son Hanoi 100000 Vietnam

This article's main content is directed toward designing and applying a new control algorithm for automotive stabilizer bars. Previous studies often only used classical control algorithms or simple fuzzy algorithms to control hydraulic anti-roll systems on cars based on safety criteria. In this article, a self-learning algorithm (ANFIS) is established based on inheriting the advantages of previous algorithms. Additionally, this algorithm is modified and improved to increase the convergence of the results after the end of the steering process. Simulations show that when the self-learning solution is applied to active anti-roll bars, the roll angle value and the attenuation of the vertical force at wheels decrease significantly. In complex motion conditions (second case, v3 ), rollover occurs if the automobile does not have an anti-roll bar. However, roll stability and road holding ability are always ensured when applying the ANFIS algorithm to control active anti-roll bars. According to these findings, the minimum value of the vertical force at the rear wheel can be up to 1384.02 N even when the car is traveling in extremely harsh conditions (second case, v4 ). In addition, the response speed and convergence of values are always well-controlled when the intelligent self-learning algorithm is applied to the anti-roll control system.

关键词： ANFIS control active stabilizer bar self-learning algorithm rollover phenomenon vehicle dynamic

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Development of Casting Curve of Copper Anode Plate Based on self-learning algorithm

Development of Casting Curve of Copper Anode Plate Based on ...

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第35届中国控制与决策会议

作者： Cao Yu Teng Shungao Liu Jingmin Yunnan Vocational College of land and resources Yunnan Copper Stock Southwest Copper Branch

Based on the understanding of the changing characteristics of working conditions in the casting process of copper anode plate,on the basis of establishing a good mathematical model,the smoothness control of copper water flow rate in the casting process and the fallback control of the casting package are completed by using a self-learning algorithm,so as to realize the accurate control of the weight of the copper anode *** a period of debugging and trial operation in a copper company,the correctness and adaptability of the control algorithm were verified.

关键词： Copper anode plate Pull-back control self-learning algorithm Mathematical model

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A self-learning algorithm for estimating solar heat gain and temperature changes in a single-Family residence

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ENERGY AND BUILDINGS 2017年 150卷 100-110页

作者： Omar, Farhad Bushby, Steven T. Williams, Ronald D. NIST Engn Lab Mech Syst & Controls Gaithersburg MD 20899 USA Univ Virginia Charles L Brown Dept Elect & Comp Engn Charlottesville VA 22904 USA

To develop effective control optimization strategies for managing residential electricity consumption in a smart grid environment, predictive algorithms are needed that are simple to implement, minimize custom configuration, and provide sufficient accuracy to enable meaningful control decisions. This paper presents a self-learning algorithm for predicting indoor temperature in a single-family residence using a first-order lumped capacitance (1 R1 C) model that can be used to evaluate the consequences of energy saving or load shifting strategies on thermal comfort. The algorithm is formulated in such a way that key design details such as window size/configuration, thermal insulation, and airtightness (all major factors that affect heat loss and solar heat gain) are combined into effective parameters that can be learned from observation. This approach eliminates the need for a custom configuration for each residence. The model was validated using experimental data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility. It was found that with a simple temperature decay test to determine a thermal time constant and a seven-day sliding window of training data to account for seasonal variations in other parameters, the algorithm can reliably predict indoor temperatures for a 24h period using a solar irradiance forecast, an outdoor air temperature forecast, and heat pump output. The average root mean square temperature prediction error was found to be 2.2%. Published by Elsevier B.V.

关键词： Estimating solar heat gain Indoor temperature prediction Lumped capacitance model Overall heat transfer coefficient Residential energy control self-learning algorithm Sliding window optimization Smart grid Thermal model Thermal time constant

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Adaption of a self-learning algorithm for Dynamic Classification of Water Bodies to SPOT VEGETATION Data 15th

Adaption of a Self-Learning Algorithm for Dynamic Classifica...

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15th International Conference on Computational Science and Its Applications (ICCSA)

作者： Fichtelmann, Bernd Guenther, Kurt P. Borg, Erik German Aerosp Ctr German Remote Sensing Data Ctr D-17235 Neustrelitz Germany German Aerosp Ctr German Remote Sensing Data Ctr D-82230 Wessling Germany

ISBN: (纸本)9783319214108;9783319214092

Within the ESA CCI "Fire Disturbance" project a dynamic self-learning water masking approach originally developed for AATSR data was modified for MERIS-FR(S) and MERIS-RR data and now for SPOT VEGETATION (VGT) data. The primary goal of the development was to apply for all sensors the same generic principles by combining static water masks on a global scale with a self-learning algorithm. Our approach results in the generation of a dynamic water mask which helps to distinguish dark burned area objects from other different types of dark areas (e.g. cloud or topographic shadows, coniferous forests). The use of static land-water masks includes the disadvantage that land-water masks represent only a temporal snapshot of the water bodies. Regional results demonstrate the quality of the dynamic water mask. In addition the advantages to conventional water masking algorithms are shown. Furthermore, the dynamic water masks of AATSR, MERIS and VGT for the same region are presented and discussed together with the use of more detailed static water masks.

关键词： self-learning algorithm Land-water mask Interpretation Remote sensing VGT data Cloud cover

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Adaption of a self-learning algorithm for Dynamic Classification of Water Bodies to MERIS Data

Adaption of a Self-Learning Algorithm for Dynamic Classifica...

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14th International Conference on Computational Science and Its Applications (ICCSA)

作者： Fichtelmann, Bernd Borg, Erik Guenther, Kurt P. German Aerosp Ctr German Remote Sensing Data Ctr Kalkhorstweg 53 D-17235 Neustrelitz Germany German Remote Sensing Data Ctr D-82230 Wessling Germany

ISBN: (纸本)9783319091440;9783319091433

In many global applications of remote sensing land-water masks can improve the interpretation results. Their use can be of advantage to distinguish between different types of dark areas (e. g. cloud or topographic shadows, burned areas, coniferous forests, water areas). On one hand, water bodies cannot always be classified exactly on basis of available remote sensing data. On the other hand static land-water masks of different quality are available. But the main deficiencies are caused by the fact that land-water masks represent only a temporal snapshot of the water bodies. A dynamic self-learning water masking approach was developed at first for AATSR data to combine the advantages of static mask with results of pre-classifications. This paper presents the adaption of this procedure for MERIS remote sensing data. As before with AATSR data the aim consists in integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. The results for some regional examples demonstrate the quality of masks and the advantages to conventional water masking algorithms. Furthermore, it will be discussed, that it is useful for a global water mask of high quality to integrate further special masks as cloud or in particular terrain shadow masks. At least, the land-water mask plays not only an important role in complex processing chains itself is the result of a complex procedure. Beside the results have shown successful transfer of a developed processing scheme for operational deriving of actual land-water masks to data of a second sensor, the adaption to further sensors or the adaption of the processor to other object types as e. g. forest will be possible in future as part of operational monitoring systems.

关键词： self-learning algorithm land-water mask interpretation remote sensing MERIS data cloud cover

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Artificial Neural Network-based Intelligent Grid Impedance Identification Method for Grid-Connected Inverter

Artificial Neural Network-based Intelligent Grid Impedance I...

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International Power Electronics Conference (IPEC-Himeji - ECCE Asia)

作者： Qiu, Yuan Wang, Yanbo Tian, Yanjun Chen, Zhe Aalborg Univ AAU Energy Aalborg Denmark North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China

ISBN: (纸本)9781665416313

This paper presents an intelligent grid impedance identification method for grid-connected inverter, where artificial neural network (ANN) is presented to identify time-varying grid impedance. The ANN is first trained offline by self-learning algorithm, which formulates an intelligent grid impedance identification method. Then, grid-connected inverter can identify variation of grid impedance according to output current. Simulation results are given to validate the proposed impedance identification method. The proposed impedance identification method can dynamically estimate time-varying grid impedance with good self-learning capability, so as to support the integration of renewable energies into grid.

关键词： Grid impedance identification artificial neural network intelligence self-learning algorithm grid-connected inverter time-varying impedance

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Iterative geostatistical seismic inversion with adaptive local variogram models

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Earth Science Informatics 2025年第3期18卷 1-11页

作者： Esmaeilzadeh, Shaghayegh Moradzadeh, Ali Asghari, Omid Mohebian, Reza School of Mining Engineering College of Engineering University of Tehran Tehran Iran

Seismic inversion techniques transform seismic amplitude data into quantitative representations of subsurface elastic properties. Stochastic seismic inversion methods are particularly valuable for assessing uncertainty in the spatial distribution of elastic parameters. Iterative geostatistical inversion approaches employ stochastic simulation to generate and adjust models, typically relying on a global variogram to model spatial continuity. However, in complex, non-stationary geological settings, a single variogram fails to capture spatial heterogeneity, leading to suboptimal inversion results. This study presents an innovative 3D geostatistical seismic inversion technique that adaptively updates local variograms by incorporating mismatches between predicted and observed seismic data. By dynamically refining variogram models, the approach addresses the limitations of predefined global variograms. The proposed method, GSI-ALV, is validated using a 3D synthetic non-stationary dataset and benchmarked against Global Stochastic Inversion (GSI), which employs a fixed global variogram. Both methods used identical parameterization, but GSI-ALV achieved a high global correlation coefficient of 0.9 between predicted and observed seismic data, whereas GSI achieved only 0.71. The results demonstrate that dynamically refining variogram models enhances spatial consistency, improves geological realism, and better captures complex subsurface structures compared to conventional methods. Additionally, the proposed approach is specifically designed for 3D seismic inversion and offers significantly lower computational cost than other iterative variogram updating strategies, making it well-suited for large-scale applications.

关键词： Stochastic seismic inversion self-learning algorithm Local spatial continuity models Direct sequential simulation 3D problem Hierarchical clustering

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self-learning Optimal Cruise Control Based on Individual Car-Following Style

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第10期22卷 6622-6633页

作者： Chu, Hongqing Guo, Lulu Yan, Yongjun Gao, Bingzhao Chen, Hong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China Univ Georgia Ctr Cyber Phys Syst Athens GA 30602 USA

This study aims to develop an optimal cruise controller that can automatically adapt to individual car-following style. First, the adaptive cruise control (ACC) problem is formulated as a linear quadratic optimal control, and an optimal control law containing the longitudinal acceleration of the target vehicle is derived. Then, a certain number of individual car-following styles are predefined on the basis of the proposed optimal cruise controller. Thereafter, a car-following style learning algorithm is proposed to quantify the closeness of the predefined individual car-following style to the specific driver, and a proper style is thus determined for the specific driver by using this learning algorithm. On the basis of the learned car-following style, the proposed optimal cruise controller can adapt itself to individual car-following style. Finally, the proposed self-learning optimal cruise controller is evaluated through simulation and experimental tests. Results show that the control behavior of the proposed self-learning optimal controller is closer to that of the human driver than that of a factory-installed ACC.

关键词： Cruise control Acceleration Optimal control Adaptation models Vehicle dynamics Automobiles Adaptive cruise control reinforcement learning linear quadratic regulator driving style self-learning algorithm

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Parameter Auto-tuning Method Based on self-learning algorithm

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JOURNAL OF COMPUTERS 2012年第9期7卷 2168-2175页

作者： Ao, Chaohua Bi, Jianchao Chongqing Ind Polytech Coll Dept Automat Chongqing Peoples R China Chongqing Univ Coll Automat Chongqing Peoples R China

The central air condition system is a complex system. Aimed at the puzzle of optimal status adjusting by once setting parameter of fuzzy PID, the paper proposed a sort of parameter auto-tuning method of fuzzy-PID based on self-learning algorithm. It adopted parameter autotuning technique to adjust the PID parameters in real time so as to ensure good quality of control system. It combined fuzzy logic control with classical PID control, and then made the fuzzy auto-tuning of PID parameter on line, after that, the parameters of adjusted system were switched to the natural work status. Once the change of system performance occurred, and it went beyond the specified range, then the system would automatically start the parameter tuning process of PID. The engineering practice shows that the retuned parameters could obtain better control effect than before, therefore the system performance is greatly enhanced, and it is higher in control accuracy, better in stability, stronger in robustness.

关键词： parameter auto-tuning fuzzy PID controller self-learning algorithm

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