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Model Reference Adaptive Control of Semi-active Suspension Model Based on adaboost algorithm for Rollover Prediction

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SAE INTERNATIONAL JOURNAL OF VEHICLE DYNAMICS STABILITY AND NVH 2022年第1期6卷 71-86页

作者： Zhu Tianjun Wan, Hegao Wang, Zhenfeng Wei, Ma Xu, Xuejiao Zou Zhiliang Du Sanmiao Hebei Univ Engn Coll Mech & Equipment Engn Handan Peoples R China Zhaoqing Univ Dept Mech & Automot Engn Zhaoqing Peoples R China CATARC Tianjin Automot Engn Res Inst Co Ltd Tianjin Peoples R China JA SOLAR Xingtai Co Ltd Xingtai Peoples R China Nanchang Univ Sch Econ & Management Nanchang Jiangxi Peoples R China

Due to their large volume structure, when a heavy vehicle encounters sudden road conditions, emergency turns, or lane changes, it is very easy for vehicle rollover accidents to occur;however, well-designed suspension systems can greatly reduce vehicle rollover occurrence. In this article, a novel semi-active suspension adaptive control based on adaboost algorithm is proposed to effectively improve the vehicle rollover stability under dangerous working conditions. This research first established a vehicle rollover warning model based on the adaboost algorithm. Meanwhile, the approximate skyhook damping suspension model is established as the reference model of the semi-active suspension. Furthermore, the model reference adaptive control (MRAC) system is established based on Lyapunov stability theory, and the adaptive controller is designed. Finally, on the same road condition, the rollover warning control simulations are carried out under the following conditions: the 180-degree step, the fishhook, and the double-lane-change condition. Simulation results show that the proposed reference adaptive control based on the adaboost algorithm for rollover control can effectively predict vehicle rollover in early warning and improve the anti-rollover capability of vehicles.

关键词： adaboost algorithm Warning rollover Semi-active suspension Model reference adaptive control

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Comparison of four adaboost algorithm based artificial neural networks in wind speed predictions

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ENERGY CONVERSION AND MANAGEMENT 2015年 92卷 67-81页

作者： Liu, Hui Tian, Hong-qi Li, Yan-fei Zhang, Lei Cent S Univ Sch Traff & Transportat Engn Key Lab Traff Safety Track Minist Educ Changsha 410075 Hunan Peoples R China Univ Rostock Fac Informat & Elect Engn Inst Automat D-18119 Rostock Mecklenburg Vor Germany

The technology of wind speed prediction is important to guarantee the safety of wind power utilization. In this paper, four different hybrid methods are proposed for the high-precision multi-step wind speed predictions based on the adaboost (Adaptive Boosting) algorithm and the MLP (Multilayer Perceptron) neural networks. In the hybrid adaboost-MLP forecasting architecture, four important algorithms are adopted for the training and modeling of the MLP neural networks, including GD-ALR-BP algorithm, GDM-ALR-BP algorithm, CG-BP-FR algorithm and BFGS algorithm. The aim of the study is to investigate the promoted forecasting percentages of the MLP neural networks by the adaboost algorithm' optimization under various training algorithms. The hybrid models in the performance comparison include adaboost-GD-ALR-BP-MLP, adaboost-GDM-ALR-BP-MLP, adaboost-CG-BP-FR-MLP, adaboost-BFGS-MLP, GD-ALR-BP-MLP, GDM-ALR-BP-MLP, CG-BP-FR-MLP and BFGS-MLP. Two experimental results show that: (1) the proposed hybrid adaboost-MLP forecasting architecture is effective for the wind speed predictions;(2) the adaboost algorithm has promoted the forecasting performance of the MLP neural networks considerably;(3) among the proposed adaboost-MLP forecasting models, the adaboost-CG-BP-FR-MLP model has the best performance;and (4) the improved percentages of the MLP neural networks by the adaboost algorithm decrease step by step with the following sequence of training algorithms as: GD-ALR-BP, GDM-ALR-BP, CG-BP-FR and BFGS. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Wind energy Wind speed forecasting Wind speed predictions adaboost algorithm Neural networks

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Face detection based on adaboost algorithm with Local AutoCorrelation image

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IEICE ELECTRONICS EXPRESS 2010年第15期7卷 1125-1131页

作者： Zhu, Hongjin Koga, Toshio Yamagata Univ Grad Sch Sci & Engn Yonezawa Yamagata 9928510 Japan

This paper proposes a novel concept of Local AutoCorrelation (LAC), which is converted to LAC images for robust face detection under varying illumination conditions. We have applied LAC images to face detection based on adaboost algorithm. MIT CBCL images are used for training, while CMU PIE face database including a variety of illumination directions is used for detection. As a result, the LAC is found to be so effective that face detection rate is improved by up to 4.7%.

关键词： adaboost algorithm face detection Local AutoCorrelation illumination normalization

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Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With adaboost algorithm

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IEEE ACCESS 2021年 9卷 94040-94052页

作者： An, Guoqing Jiang, Ziyao Cao, Xin Liang, Yufei Zhao, Yuyang Li, Zheng Dong, Weichao Sun, Hexu Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Hebei Peoples R China Hebei Engn Lab Wind Power Photovolta Coupling Hyd Shijiazhuang 050018 Hebei Peoples R China Hebei Construct & Investment Grp New Energy Co Lt Shijiazhuang 050051 Hebei Peoples R China

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

关键词： adaboost algorithm extreme learning machine optimization algorithm wind power prediction

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Intelligent islanding detection method for photovoltaic power system based on adaboost algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第18期14卷 3630-3640页

作者： Ke, Jia Zhengxuan, Zhu Zhe, Yang Yu, Fang Tianshu, Bi Jiankang, Zhang North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China Northwest Branch State Grid Corp China Xian Peoples R China

The universal islanding detection methods (IDMs) for photovoltaic (PV) power systems require manually thresholds setting. That will lead to a certain non-detection zone (NDZ). Moreover, disturbance signals injected by active detection methods may adversely affect power quality. Aiming at the above problems, this study proposes a passive intelligent IDM for parallel multi-PV system based on improved Adaptive Boosting (adaboost) algorithm. Using adaboost algorithm to generate classification models for islanding detection can theoretically avoid the NDZ of passive methods. The proposed method takes advantage of the electrical connection between characteristic parameters to adjust the classification model and improves the detection ability by redistributing the weight of each sub-model. Simulation results show that when adopted to a multi-PV system, the proposed method can effectively distinguish islanding operation in the NDZs of conventional passive IDMs. The method can also achieve accurate detection in the case of short-term power quality interferences, line faults and disturbance signal interference injected by active methods.

关键词： learning (artificial intelligence) pattern classification power supply quality power distribution faults photovoltaic power systems intelligent islanding detection method photovoltaic power system adaboost algorithm universal islanding detection methods PV nondetection zone NDZ disturbance signals active detection methods passive intelligent IDM improved Adaptive Boosting algorithm classification model passive methods islanding operation short-term power quality interferences disturbance signal interference active methods parallel multi-PV system

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Research on facial feature-based gender intelligent recognition based on the adaboost algorithm

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INTERNATIONAL JOURNAL OF BIOMETRICS 2021年第1期13卷 40-50页

作者： Wang, Jing Henan Univ Anim Husb & Econ Dept Energy & Intelligence Engn Zhengzhou 450011 Peoples R China

In order to overcome the problem of poor facial recognition intelligence and weak gender judgment, a new method based on adaboost algorithm for facial feature-based gender intelligence recognition is proposed in this paper. In this method, the three-dimensional special point detection, weak perspective projection, spatial region segmentation and other methods are employed to construct the facial feature information sampling model. The adaboost algorithm is used to analyse the matching between facial features and gender, on which facial-feature gender intelligent recognition is performed according to the distribution of the eyes, nose and mouth of the face image, and the edge contour detection model of the face image is constructed. The experimental results show that the method has the advantages of good intelligence, high recognition precision and short time cost in face-based gender recognition.

关键词： adaboost algorithm facial features intelligent identification gender

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Parameter Identification and Model Optimization of Cold Rolled Strip Based on GRA-AHP and adaboost-SA algorithm 24

Parameter Identification and Model Optimization of Cold Roll...

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Proceedings of the 2024 International Conference on Mathematics and Machine Learning

作者： Chuanxu Wang Tingting Yan Yuchan Zhang Shenghe Xue School of Mechanical Engineering Shenyang Jianzhu University Shenyang Liaoning China School of Science Shenyang Jianzhu University Shenyang Liaoning China School of Municipal and Environmental Engineering Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (纸本)9798400711657

In this paper, Entropy Weight Method (EWM), Grey Relational Analysis method (GRA), and Analytic Hierarchy Process (AHP) are innovatively combined to construct a comprehensive evaluation system, namely GRA-AHP model. This model is used to quantify and identify the key parameters of mechanical properties of cold rolled strip steel. Aiming at the problem of hardness optimization in cold rolled strip production, this paper provides a method of combining polynomial fitting and integrated algorithm. Then an on-line monitoring model of strip hardness based on adaboost algorithm is established. The adaboost algorithm is incorporated into the detection regression model, and the expression of on-line monitoring of strip quality ensemble regression model is given. Through the analysis of feature importance, the prediction ability of the model is further optimized. The high precision and stability of the model are ensured. This paper aims to transform the research results into practical industrial applications, broaden the scope of application of the model, and improve its adaptability and efficiency under different production conditions.

关键词： adaboost algorithm

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Advanced Pedestrian Detection System Using Combination of Haar-like Features, adaboost algorithm and Edgelet-Shapelet

Advanced Pedestrian Detection System Using Combination of Ha...

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3rd IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

作者： Rakate, G. R. Borhade, S. R. Jadhav, P. S. Shah, M. S. Vishwakarma Inst Technol Pune Maharashtra India

ISBN: (纸本)9781467324816;9781467313445

The basic task in various applications like automotive control, video surveillance, etc is human body detection. For such applications to be successful, high accuracy and high speed performance are crucial. Image feature description determines accuracy and hence it should be robust against occlusion, rotation, and changes in object shapes and illumination conditions. Till date, many such feature descriptors have been proposed. Many of them are based on histogram of oriented gradients (HOG) along with support vector machine (SVM) classifier. Limitation of this method is high time consumption though it achieved good performance for Pedestrian Detection. To counter this limitation, a Two-step framework was proposed. It consisted two steps - full-body detection (FBD) and head-shoulder detection (HSD). Zhen Li proposed fusion of Haar-like and HOG features for better performance, and HSD step utilizes Edgelet features for classification and detection. But this method results in low detection rate and less computation speed. To counter these limitations, we have proposed an advanced method to improve both detection rate and speed. We achieve this by combination of Haar-like and Triangular features for FBD and Edgelet/Shapelet for HSD. We have achieved an average 95% detection rate and 60% faster speed for this proposed method.

关键词： Pedestrian Detection Histogram of Oriented Gradients Haar-like Features adaboost algorithm Edgelet/Shapelet

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Optimization of adaboost algorithm by PSO and Its Application in Translation Initiation Sites Prediction

Optimization of AdaBoost Algorithm by PSO and Its Applicatio...

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1st WRI Global Congress on Intelligent Systems (GCIS 2009)

作者： Gao, Shi-Bo Zhang, Yun-Tao China W Normal Univ Inst Appl Chem Nanchong Sichuan Peoples R China

ISBN: (纸本)9780769535715

In this paper, we optimize the boosted results of adaboost algorithm by particle swarm optimization (PSO) and form a learning algorithm PSO-AB. We use it to boost the classification ability of support vector machine (SVM) and back propagation neural network (BPNN). This PSO-AB adopts SVM and BPNN to classify the experimental data, uses adaboost algorithm to boost the classification results, and then uses PSO to optimize the boosted results. Translation initiation sites (TIS) prediction results show that adaboost algorithm could improve the accuracy of training set, but for testing set the result is not satisfactory. PSO-AB makes it possible to maximize the testing accuracy of adaboost algorithm on the premise of that the accuracy of training set is still exact. PSO-AB will be a more effective method to classification problems compared with adaboost algorithm and base learning algorithm.

关键词： adaboost algorithm Particle Swarm Optimization Translation Initiation Sites Optimization

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Yarn Quality Prediction for Small Samples Based on adaboost algorithm

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Journal of Donghua University(English Edition) 2023年第3期40卷 261-266页

作者：刘智玉陈南梁汪军 College of Textiles Donghua UniversityShanghai 201620China Key Laboratory of Textile Science&Technology Ministry of EducationDonghua UniversityShanghai 201620China

In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an adaboost algorithm(adaboost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for *** prediction experiments of the yarn evenness and the yarn strength were *** coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these *** the prediction experiments,the determination coefficient of the yarn evenness prediction result of the adaboost model is 76% and 87% higher than that of the LR model and the MLP model,*** determination coefficient of the yarn strength prediction result of the adaboost model is slightly higher than that of the other two *** that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the adaboost model has the best adaptability for the nonlinear dataset among the three *** addition,the adaboost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample *** is proved that the adaboost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.

关键词： stability and generalization ability for small samples.Key words:yarn quality prediction adaboost algorithm small sample generalization ability

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