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radial basis function neural network sliding mode control-based direct yaw moment control for electrically driven vehicles

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INTERNATIONAL JOURNAL OF VEHICLE PERFORMANCE 2025年第2期11卷 125-158页

作者： Deng, Zhaowen Hu, Wenqian Gao, Wei Guo, Yaohua Fan, Qingsong Hubei Univ Automot Technol Hubei Key Lab Automot Power Transmiss & Elect Cont Shiyan 442002 Hubei Peoples R China Hubei Univ Automot Technol Inst Automot Engineers Shiyan 442002 Peoples R China Yutong Bus Co Ltd Zhengzhou 450000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Guizhou Univ Sch Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China

To improve the lateral stability of distributed electric vehicles, a neural network sliding mode direct yaw moment control (DYC) strategy with hierarchical control was proposed to address the nonlinear dynamic characteristics and poor robustness of the vehicle model under extreme conditions. As well as to control the vehicle to track the expected trajectory, a position-orientation optimal acceleration preview driver model was established based on the optimal acceleration theory of the positional orientation preview. Firstly, a new two-degree-of-freedom (2-DOF) nonlinear control equation of state was derived based on the conventional 2-DOF linear control equation of state. Then, a hierarchical controller was designed in this way, and the radial basis function neural network (RBFNN) sliding mode controller (SMC) was used in the upper layer to calculate the total target additional yaw moment of the vehicle. The lower controller is optimised to minimise tyre adhesion utilisation. It is a torque distribution controller for hub motors that utilises quadratic programming. Finally, simulation tests were performed using CarSim and Simulink. As indicated by the simulation findings, the established controller's better robustness and control accuracy over the SMC and PID controllers improves the vehicle's lateral stability and driving safety.

关键词： distributed drive electric vehicle radial basis function neural network RBFNN sliding mode control lateral stability

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Multi-objective optimization of supply air inlet structure for impinging jet ventilation system based on radial basis function neural network

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CASE STUDIES IN THERMAL ENGINEERING 2025年 65卷

作者： Wang, Chen Hu, Ke Liu, Yin Zhongyuan Univ Technol Sch Smarts Energy & Environm Zhengzhou 451191 Peoples R China

A multi-objective optimization of the supply air inlet structure for Impinging Jet Ventilation (IJV) was conducted based on the radial basis function neural network (RBFNN) and using a genetic optimization algorithm. The Predicted Mean Vote at the occupant's ankle level (PMV0.1) and the Energy Utilization Coefficient (Et) exhibited significant variability across different inlet structures, thus they were selected as optimization objectives. The predicted results showed substantial consistency with numerical simulations. Within the selected parameter range, the optimal PMV0.1 value was -0.17, and the optimal Etvalue was 3.57. Furthermore, by adjusting the weights of different optimization objectives, suitable structural parameters can be determined. It was also concluded that, for the given indoor ventilation conditions, the length of the supply air inlet structure should be shorter than its width to better enhance the PMV0.1 value in the areas surrounding occupants.

关键词： radial basis function neural network Genetic optimization algorithm Impinging jet ventilation Supply air inlet structure Indoor ventilation

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Design of a Sliding Mode Controller for Lateral Motion Control of a Supercavitating Vehicle Based on a radial basis function neural network Observer

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JOURNAL OF MARINE SCIENCE AND ENGINEERING 2025年第3期13卷 418-418页

作者： Chen, Shangze Zhao, Xinhua Ye, Xiufen Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

Newton's second law has been applied to create a dynamic model of the lateral motion of a supercavitating vehicle, assuming a stable cavity. However, some states cannot be measured, and there is uncertainty in the lateral model. Aiming to resolve these issues, this work describes the design of a sliding-mode controller based on a state observer and a radial basis function neural network observer to realize lateral attitude control of supercavitating vehicle. Because there is a variable signal that cannot always be obtained in the established lateral model, the state observer is designed to estimate the unknown state;when there are unknown variables and model uncertainty and the state is unknown, the radial basis function neural network observer estimates model uncertainty. Finally, based on these results, a robust sliding mode controller is designed and system stability is proved by the Lyapunov theorem.

关键词： lateral motion supercavitating vehicle radial basis function neural network observer

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Robust adaptive nonlinear PID controller using radial basis function neural network for ballbots with external force

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 61卷

作者： Nguyen, Van-Truong Nguyen, Quoc-Cuong Van, Mien Su, Shun-Feng Garg, Harish Duong, Dai-Nhan Tan, Phan Xuan Hanoi Univ Ind Fac Mechatron SMAE Hanoi 11900 Vietnam Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Ctr Intelligent Autonomous Mfg Syst i AMS Belfast BT9 5AG North Ireland Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan Thapar Inst Engn & Technol Deemed Univ Dept Math Patiala 147004 Punjab India Shibaura Inst Technol Coll Engn Tokyo 1358548 Japan

This paper presents a new adaptive nonlinear proportional integral derivative radial basis function neural network (NPID-RBFNN) for ballbots with external force. The proposed controller is designed based on a hybrid of a nonlinear proportional integral derivative (NPID) control, radial basis function neural networks (RBFNN), and balancing composite motion optimization (BCMO). The hybrid NPID-RBFNN controller offers a light-weight computation, chattering-reduction, while providing high robustness against model uncertainties and external disturbance. Therefore, it provides excellent features to control ballbots against the counterpart approaches such as the conventional PID, conventional NPID, which preserves low robustness against disturbances, or sliding mode control (SMC), which provides higher chattering. The BCMO is used to determine the gain values that best fit the system, and RBFNN is learned continuously during the ballbot movement to balance the system in the most stable and smooth way. The NPID-RBFNN controller is proven to be stable through the Lyapunov approach. The simulation and experiment results show that the NPID-RBFNN controller is a robust method for controlling the ballbot system's motion in applications with external force.

关键词： Ballbot Nonlinear PID control radial basis function neural network Balancing composite motion optimization External force

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Aggregated approach for interstitial lung diseases classification using attention based CNN and radial basis function neural network

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SYSTEMS AND SOFT COMPUTING 2025年 7卷

作者： Kumarganesh, S. Shree, K. V. M. Rishabavarthani, P. Ganesh, C. Anthoniraj, S. Thiyaneswaran, B. Dang, Lam Sagayam, K. Martin Dinh, Linh Dang, Hien Knowledge Inst Technol Dept ECE Salem Tamil Nadu India Dhanalakshmi Srinivasan Engn Coll Dept AI & DS Preambular Tamil Nadu India Sri Ramakrishna Engn Coll Dept ECE Coimbatore Tamil Nadu India Sri Eshwar Coll Engn Dept CCE Coimbatore Tamil Nadu India JAIN Deemed Univ Dept CSE Bangalore India Sona Coll Technol Dept ECE Salem Tamil Nadu India INSA Lyon Dept Comp Sci Villeurbanne France Karunya Inst Technol & Sci Dept ECE Coimbatore Tamil Nadu India Suffolk Univ Dept Informat Syst Boston MA USA Thuyloi Univ Fac Comp Sci & Engn Hanoi Vietnam Molloy Univ Dept Math & Comp Sci Rockville NY USA

The medical field has significantly advanced with advances in technology, with a focus on biomedical devices and early diagnosis. Image processing techniques and artificial intelligence are used to analyze the lung anatomy and ensure an accurate diagnosis of interstitial lung diseases. This study proposes an automated approach for identifying Interstitial Lung Diseases (ILD) using biomedical images. Computed Tomography (CT) biomedical images were used for analysis. This CT image was analyzed using both radiomic and deep learning features for efficient identification of ILD at an early stage. Here, radiomic features were extracted using gray-level properties and reduced using Particle Swarm optimization with inverse maximization of accuracy and precision as objective functions. The reduced features were then trained and tested using a radial basis function neural network (RBFNN). In parallel, an attention-based convolutional neural network was used to perform deep learning-based ILD classification using gray and local pattern images. Finally, both model outputs were aggregated for the final prediction by evaluating accuracy, precision, and F1-score. The proposed approach outperformed the ensemble approach for ILD classification by increasing its accuracy to 5 % for final prediction.

关键词： Interstitial lung diseases Computed tomography CNN Classification radial basis function neural network

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A novel radial basis function neural network classifier based on three-way decisions

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 141卷

作者： Li, Tengbiao Qiao, Junsheng Northwest Normal Univ Coll Math & Stat Lanzhou 730070 Peoples R China Gansu Prov Res Ctr Basic Disciplines Math & Stat Lanzhou Peoples R China

The radial basis function neural network (RBFNN) has gained widespread adoption due to its simple network topology and excellent learning capability. However, the performance of empirical formula-based RBFNN tends to deteriorate when faced with sample imbalance issues. In order to fully harness the potential of RBFNN classifier, inspired by three-way decisions (TWDs) conforming to the logic of human behavior, this paper investigates a novel classifier named TWDs-RBFNN that integrates TWDs and RBFNN. This novel artificial intelligence approach efficiently handles imbalanced samples in imbalanced classification tasks while achieving enhanced performance within amore streamlined topology. TWDs-RBFNN incorporates a TWDs module into the RBFNN framework, which dynamically stores and efficiently trains difficult-to-classify samples for reinforcement learning of intricate sample information and network topology optimization through parameter updates. Drawing upon current advancements in neuroscience and granule computing theory, we introduce gradient harmonizing mechanism to balance samples at the loss function level and handle outliers to avoid parameter bias and improve training efficiency. We also introduce information granules and establish a novel conditional probability formula to safeguard against information loss. The results of numerous experiments on imbalanced datasets demonstrate that TWDs effectively enhances the compactness of the network structure and improve its generalization ability in RBFNN. Specifically, we provide theoretical proof for the convergence of TWDs-RBFNN to analyze their effectiveness.

关键词： radial basis function neural network network topology Three-way decisions Information granule Imbalanced classification

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Application of unfolded principal component analysis-radial basis function neural network for determination of celecoxib in human serum by three-dimensional excitation-emission matrix fluorescence spectroscopy

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2015年 138卷 675-683页

作者： Shahlaei, Mohsen Bahrami, Gholamreza Abdolmaleki, Sajjad Sadrjavadi, Komail Majnooni, Mohammad Bagher Kermanshah Univ Med Sci Nano Drug Delivery Res Ctr Sch Pharm Kermanshah *** Iran Kermanshah Univ Med Sci Med Biol Res Ctr Kermanshah *** Iran Kermanshah Univ Med Sci Student Res Comm Kermanshah *** Iran Kermanshah Univ Med Sci Novel Drug Delivery Res Ctr Sch Pharm Kermanshah *** Iran

This study describes a simple and rapid approach of monitoring celecoxib (CLX). Unfolded principal component analysis-radial basis function neural network (UPCA-RBFNN) and excitation-emission spectra were combined to develop new model in the determination of CDC in human serum samples. Fluorescence landscapes with excitation wavelengths from 250 to 310 nm and emission wavelengths in the range 280-450 nm were obtained. The figures of merit for the developed model were evaluated. High performance liquid chromatography (HPLC) technique was also used as a standard method. Accuracy of the method was investigated by analysis of the serum,samples spiked with various concentration of CLX and a recovery of 103.63% was obtained. The results indicated that the proposed method is an interesting alternative to the traditional techniques normally used for determining CLX such as HPLC. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Celecoxib Excitation-emission fluorescence matrices Principal component analysis radial basis function neural network

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radial basis function neural network vibration control of a flexible planar parallel manipulator based on acceleration feedback

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JOURNAL OF VIBRATION AND CONTROL 2022年第3-4期28卷 351-363页

作者： Yu, Long-huan Qiu, Zhi-cheng Zhang, Xian-min South China Univ Technol Guangdong Key Lab Precis Equipment & Mfg Technol Guangzhou Guangdong Peoples R China

The self-excited vibration of flexible planar 3-RRR parallel manipulators is converted from the residual vibration after high-speed motion and is a resonance of the strongly coupled and nonlinear electromechanical system. This makes the active vibration control quite a challenging task. In this study, we attempt to adopt the radial basis function neural network control algorithm based on acceleration feedback for suppressing the self-excited vibration and guarantee its position accuracy. The stability of the controlled system is proved by the Lyapunov concept. Self-excited vibration control experiments are conducted near the singular region. Experimental results demonstrate the effectiveness of our adopted controller.

关键词： Flexible planar 3-RRR parallel manipulator self-excited vibration control acceleration feedback radial basis function neural network experiments

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radial basis function neural network for the Evaluation of Image Color Quality Shown on Liquid Crystal Displays

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IEEE ACCESS 2021年 9卷 21694-21707页

作者： Campo, Felipe Arias Del Villegas, Osslan Osiris Vergara Sanchez, Vianey Guadalupe Cruz Ochoa Dominguez, Humberto De Jesus Nandayapa, Manuel Univ Autonoma Ciudad Juarez Inst Ingn & Tecnol Juarez 32310 Chihuahua Mexico

The color quality of an image shown on a liquid crystal display (LCD) can be measured with a spectroradiometer;however, this instrument is expensive, work under controlled illumination conditions with an artificial source of light, and measurements take a long time. A spectroradiometer returns measurements of wavelength or CIE color space. A low-cost and fast alternative consists of using a digital camera that outputs RGB measurements. Unfortunately, comparisons between measurements obtained with both instruments cannot be performed;hence, conversion equations must be used. The main problem is that equations do not consider the effects caused by the camera lens, sensor variations, and configurable parameters such as gain and the exposure time. This paper proposes the architecture of a radial basis function neural network (RBFNN) to measure the image color quality displayed by an LCD using a digital camera. The RGB values acquired with a camera are used as inputs to the RBFNN. The output predicted the luminance and chromaticity components in the CIExyY color space and included the corrections to the lens and camera parameters. First, the RBFNN topology is explained, including the calculation of the number of neurons in the hidden layer, and the definition of the dispersion centers and their associated spread. Next, the experiments related to RGB color space reconstruction and conversion from RGB to CIE are presented. The proposed approach was tested on a real automotive scenario. The results obtained were similar to those measured with the spectroradiometer with an accuracy of 93.3%. Moreover, the results remained within limits established by the six-sigma methodology.

关键词： Image color analysis Spectroradiometers Wavelength measurement Liquid crystal displays Digital cameras Proposals Lenses Liquid crystal display radial basis function neural network digital camera spectroradiometer RGB CIE

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radial basis function neural network for solution of two-dimensional burgers' equation

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PHYSICA SCRIPTA 2024年第6期99卷 066002-066002页

作者： Kapoor, Mamta Rawal, Kirti Lovely Profess Univ Jalandhar 144411 India Lovely Profess Univ Dept Elect Phagwara Punjab India

In present research a neural network-based method named as 'radial basis function neural network method' is tested upon two-dimensional Burgers' equation. For this purpose, five types of the radial basis functions are considered such as;Linear, Cubic, Quintic, Multiquadric, and gaussian. The validation of results is provided via L infinity error, Mean Square Error, and the Root Mean Square Error. The obtained errors are acceptable from mathematical view point. The graphical compatibility of the results is also tested via the comparison of exact and predicted solutions in the graphs. It is a novel viewpoint to tackle the complex natured mathematical problems. In such method there is no discretization, linearization or quasi-linearization is provided. Therefore, no such scope of error is present. The present method will surely be a major breakthrough to tackle numerous mathematical models.

关键词： two-dimensional burgers' equation radial basis function neural network different types of radial basis

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