Artificial Intelligence Review最新文献_第2页

Graph pooling for graph-level representation learning: a survey 用于图级表示学习的图池：一项调查

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-10949-2

Zhi-Peng Li, Si-Guo Wang, Qin-Hu Zhang, Yi-Jie Pan, Nai-An Xiao, Jia-Yang Guo, Chang-An Yuan, Wen-Jian Liu, De-Shuang Huang

{"title":"Graph pooling for graph-level representation learning: a survey","authors":"Zhi-Peng Li, Si-Guo Wang, Qin-Hu Zhang, Yi-Jie Pan, Nai-An Xiao, Jia-Yang Guo, Chang-An Yuan, Wen-Jian Liu, De-Shuang Huang","doi":"10.1007/s10462-024-10949-2","DOIUrl":"10.1007/s10462-024-10949-2","url":null,"abstract":"<div><p>In graph-level representation learning tasks, graph neural networks have received much attention for their powerful feature learning capabilities. However, with the increasing scales of graph data, how to efficiently process and extract the key information has become the focus of research. The graph pooling technique, as a key step in graph neural networks, simplifies the graph structure by merging nodes or subgraphs, which significantly improves the computational efficiency and feature extraction ability of graph neural networks. Although various graph pooling methods have been proposed by numerous scholars, there is still a relative lack of systematic summaries of these works. In this paper, we comprehensively sort out the fundamentals and recent progress of graph pooling techniques in graph neural networks and discuss its wide range of application scenarios, as well as the current challenges and opportunities, which point out the direction for future research. Specifically, we first provide a detailed introduction to the basics of graph pooling, including its definition, principles, and its function in graph neural networks. Then, we categorize and summarize the research preliminaries of graph pooling, including various graph pooling methods proposed in recent years. Next, we explore the potential of graph pooling for a wide range of applications, which provides insightful insights for the promotion and practice of graph pooling technology in more fields. Furthermore, we conduct a comparative analysis of various graph pooling methods and evaluate their performance on a benchmark dataset, providing a comprehensive understanding of their strengths and weaknesses. Finally, we systematically analyze the challenges and opportunities of the current graph pooling methods and provide a prospective outlook on future research directions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10949-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Damage location and area measurement of aviation functional surface via neural radiance field and improved Yolov8 network 通过神经辐射场和改进的 Yolov8 网络测量航空功能表面的损伤位置和面积

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11073-x

Qichun Hu, Haojun Xu, Xiaolong Wei, Yu Cai, Yizhen Yin, Junliang Chen, Weifeng He

{"title":"Damage location and area measurement of aviation functional surface via neural radiance field and improved Yolov8 network","authors":"Qichun Hu, Haojun Xu, Xiaolong Wei, Yu Cai, Yizhen Yin, Junliang Chen, Weifeng He","doi":"10.1007/s10462-024-11073-x","DOIUrl":"10.1007/s10462-024-11073-x","url":null,"abstract":"<div><p>To realize high-precision intelligent detection, location and area measurement of aviation functional surface damage, a damage location and area measurement method combining neural radiance field and improved Yolov8 network is proposed in this paper. The high-fidelity NeRF (Neural Radiance Field) and 3DGS (3D Gaussian Splatting) models are trained by acquired multi-view optical images of damaged functional surfaces. The rendered new-view images are used as a new data augmentation method to enhance the training effect of Yolov8 network. The network architecture of Yolov8 model is improved. The backbone is replaced with the latest StarNet feature extraction network, and the context feature fusion module (CFFM) proposed in this paper is used for feature fusion enhancement. The improved context-guide multi-head self-attention (CG-MHSA) is added to the detection Head. The comparison and ablation experiment results show that the improved module proposed in this paper has a good effect on the improvement of Yolov8 model, and improves the damage detection ability and location ability of the model. The application experiment results verify the effectiveness of the proposed method for calculating the damage area of a plane/camber surface, and the accuracy of the damage area measurement is high.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11073-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications of deep learning in Alzheimer’s disease: a systematic literature review of current trends, methodologies, challenges, innovations, and future directions

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11041-5

Shiva Toumaj, Arash Heidari, Reza Shahhosseini, Nima Jafari Navimipour

{"title":"Applications of deep learning in Alzheimer’s disease: a systematic literature review of current trends, methodologies, challenges, innovations, and future directions","authors":"Shiva Toumaj, Arash Heidari, Reza Shahhosseini, Nima Jafari Navimipour","doi":"10.1007/s10462-024-11041-5","DOIUrl":"10.1007/s10462-024-11041-5","url":null,"abstract":"<div><p>Alzheimer’s Disease (AD) constitutes a significant global health issue. In the next 40 years, it is expected to affect 106 million people. Although more and more people are getting AD, there are still no effective drugs to treat it. Insightful information about how important it is to find and treat AD quickly. Recently, Deep Learning (DL) techniques have been used more and more to diagnose AD. They claim better accuracy in drug reuse, medication recognition, and labeling. This essay meticulously examines the works that have talked about using DL with Alzheimer’s disease. Some of the methods are Natural Language Processing (NLP), drug reuse, classification, and identification. Concerning these methods, we examine their pros and cons, paying special attention to how easily they can be explained, how safe they are, and how they can be used in medical situations. One important finding is that Convolutional Neural Networks (CNNs) are most often used for AD research and Python is most often used for DL issues. Some security problems, like data protection and model stability, are not looked at enough in the present research, according to us. This study thoroughly examines present methods and also points out areas that need more work, like better data integration and AI systems that can be explained. The findings should help guide more research and speed up the creation of DL-based AD identification tools in the future.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11041-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconstructing damaged fNIRS signals with a generative deep learning model

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11028-2

Yingxu Zhi, Baiqiang Zhang, Bingxin Xu, Fei Wan, Peisong Niu, Haijing Niu

{"title":"Reconstructing damaged fNIRS signals with a generative deep learning model","authors":"Yingxu Zhi, Baiqiang Zhang, Bingxin Xu, Fei Wan, Peisong Niu, Haijing Niu","doi":"10.1007/s10462-024-11028-2","DOIUrl":"10.1007/s10462-024-11028-2","url":null,"abstract":"<div><p>Functional near-infrared spectroscopy (fNIRS) imaging offers a promising avenue for measuring brain function in both healthy and diseased cohorts. However, signal quality in fNIRS data frequently encounters challenges, such as low signal-to-noise ratio or substantial motion artifacts in one or multiple measurement channels, impeding the comprehensive exploitation of the data. Developing a valid method to improve the quality of damaged fNIRS signals is crucial, particularly given the extensive use of wearable fNIRS devices in natural settings where noise issues are even more unavoidable. Here, we proposed a generative deep learning approach to recover damaged fNIRS signals in one or more measurement channels. The model captured spatial and temporal variations in the time series of fNIRS data by integrating multiscale convolutional layers, gated recurrent units (GRUs), and linear regression analyses. We trained the model on a resting-state fNIRS dataset from healthy elderly individuals and evaluated its performance in terms of reconstruction accuracy and functional connectivity matrix similarity. Collectively, the proposed model exhbited an excellent performance for the reconstruction of damaged fNIRS time series. In individual channel-level, the model can accurately reconstruct damaged fNIRS time series (mean correlation = 0.80 ± 0.14) while preserving intervariable relationships (correlation = 0.93). In multiple channel-level, the model maintained robust reconstruction accuracy and consistency in terms of functional connectivity. Our findings underscore the potential of generative deep learning techniques in reconstructing damaged fNIRS signals, providing a novel perspective for the efficient utilization of data in clinical diagnosis and brain research.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11028-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time speech emotion recognition using deep learning and data augmentation

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11065-x

Chawki Barhoumi, Yassine BenAyed

{"title":"Real-time speech emotion recognition using deep learning and data augmentation","authors":"Chawki Barhoumi, Yassine BenAyed","doi":"10.1007/s10462-024-11065-x","DOIUrl":"10.1007/s10462-024-11065-x","url":null,"abstract":"<div><p>In human–human interactions, detecting emotions is often easy as it can be perceived through facial expressions, body gestures, or speech. However, in human–machine interactions, detecting human emotion can be a challenge. To improve this interaction, Speech Emotion Recognition (SER) has emerged, with the goal of recognizing emotions solely through vocal intonation. In this work, we propose a SER system based on deep learning approaches and two efficient data augmentation techniques such as noise addition and spectrogram shifting. To evaluate the proposed system, we used three different datasets: TESS, EmoDB, and RAVDESS. We employe several algorithms such as Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), Mel spectrograms, Root Mean Square Value (RMS), and chroma to select the most appropriate vocal features that represent speech emotions. Three different deep learning models were imployed, including MultiLayer Perceptron (MLP), Convolutional Neural Network (CNN), and a hybrid model that combines CNN with Bidirectional Long-Short Term Memory (Bi-LSTM). By exploring these different approaches, we were able to identify the most effective model for accurately identifying emotional states from speech signals in real-time situation. Overall, our work demonstrates the effectiveness of the proposed deep learning model, specifically based on CNN+BiLSTM enhanced with data augmentation for the proposed real-time speech emotion recognition.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11065-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing customer preferences for hydrogen cars: a characteristic objects method approach

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11027-3

Andrii Shekhovtsov, Amirkia Rafiei Oskooei, Jarosław Wątróbski, Wojciech Sałabun

{"title":"Analyzing customer preferences for hydrogen cars: a characteristic objects method approach","authors":"Andrii Shekhovtsov, Amirkia Rafiei Oskooei, Jarosław Wątróbski, Wojciech Sałabun","doi":"10.1007/s10462-024-11027-3","DOIUrl":"10.1007/s10462-024-11027-3","url":null,"abstract":"<div><p>As hydrogen vehicles gain popularity, car manufacturers are introducing numerous models, presenting customers with the challenge of choosing the most suitable option. To address this, Multi-Criteria Decision Analysis methods are often used to evaluate and select the best alternative. This study applies the Characteristic Objects Method (COMET) to address the practical problem of selecting the most appropriate hydrogen car for decision-makers. Using data provided by manufacturers, we evaluate ten hydrogen vehicles and create six decision models based on the preferences of three decision-makers, utilizing both the recently proposed Triad Support and Expected Solution Point-COMET algorithms. The models provide insights into how customer preferences can be extracted and represented in decision models. Moreover, we analyze local weights derived from the models to understand customer expectations for hydrogen cars better. The results of our study highlight the effectiveness of the COMET approach in capturing and comparing decision-maker preferences, offering a valuable methodology framework for future applications in similar multi-criteria decision-making problems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11027-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Staying ahead of phishers: a review of recent advances and emerging methodologies in phishing detection 领先于网络钓鱼者：网络钓鱼检测的最新进展和新兴方法综述

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11055-z

S. Kavya, D. Sumathi

{"title":"Staying ahead of phishers: a review of recent advances and emerging methodologies in phishing detection","authors":"S. Kavya, D. Sumathi","doi":"10.1007/s10462-024-11055-z","DOIUrl":"10.1007/s10462-024-11055-z","url":null,"abstract":"<div><p>The escalating threat of phishing attacks poses significant challenges to cybersecurity, necessitating innovative approaches for detection and mitigation. This paper addresses this need by presenting a comprehensive review of state-of-the-art methodologies for phishing detection, spanning traditional machine learning techniques to cutting-edge deep learning frameworks. The review encompasses a diverse range of methods, including list-based approaches, machine learning algorithms, graph-based analysis, deep learning models, network embedding techniques, and generative adversarial networks (GANs). Each method is meticulously scrutinized, highlighting its rationale, advantages, and empirical results. For instance, deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), demonstrate superior detection performance, leveraging their ability to extract complex patterns from phishing data. Ensemble learning techniques and GANs offer additional benefits by enhancing detection accuracy and resilience against adversarial attacks. The impact of this review extends beyond academic discourse, informing practitioners and policymakers about the evolving landscape of phishing detection. By elucidating the strengths and limitations of existing methods, this paper guides the development of more robust and effective cybersecurity solutions. Moreover, the insights gleaned from this review lay the groundwork for future research endeavors, such as integrating contextual information, user behavior analysis, and explainable AI techniques into phishing detection systems. Ultimately, this work contributes to the collective effort to fortify digital defenses against sophisticated phishing threats, safeguarding the integrity of online ecosystems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11055-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11036-2

Giang Nguyen, Judith Sáinz-Pardo Díaz, Amanda Calatrava, Lisana Berberi, Oleksandr Lytvyn, Valentin Kozlov, Viet Tran, Germán Moltó, Álvaro López García

{"title":"Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools","authors":"Giang Nguyen, Judith Sáinz-Pardo Díaz, Amanda Calatrava, Lisana Berberi, Oleksandr Lytvyn, Valentin Kozlov, Viet Tran, Germán Moltó, Álvaro López García","doi":"10.1007/s10462-024-11036-2","DOIUrl":"10.1007/s10462-024-11036-2","url":null,"abstract":"<div><p>Machine learning is one of the most widely used technologies in the field of Artificial Intelligence. As machine learning applications become increasingly ubiquitous, concerns about data privacy and security have also grown. The work in this paper presents a broad theoretical landscape concerning the evolution of machine learning and deep learning from centralized to distributed learning, first in relation to privacy-preserving machine learning and secondly in the area of privacy-enhancing technologies. It provides a comprehensive landscape of the synergy between distributed machine learning and privacy-enhancing technologies, with federated learning being one of the most prominent architectures. Various distributed learning approaches to privacy-aware techniques are structured in a review, followed by an in-depth description of relevant frameworks and libraries, more particularly in the context of federated learning. The paper also highlights the need for data protection and privacy addressed from different approaches, key findings in the field concerning AI applications, and advances in the development of related tools and techniques.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11036-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive study on the application of machine learning in psoriasis diagnosis and treatment: taxonomy, challenges and recommendations

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11031-7

Mohsen Ghorbian, Mostafa Ghobaei-Arani, Saeid Ghorbian

{"title":"A comprehensive study on the application of machine learning in psoriasis diagnosis and treatment: taxonomy, challenges and recommendations","authors":"Mohsen Ghorbian, Mostafa Ghobaei-Arani, Saeid Ghorbian","doi":"10.1007/s10462-024-11031-7","DOIUrl":"10.1007/s10462-024-11031-7","url":null,"abstract":"<div><p>Psoriasis is a common skin disease with complex mechanisms, and its diagnosis and treatment bring many challenges. In recent years, machine learning (ML) techniques have been proposed as a new tool to improve this disease’s diagnosis and treatment process. With the ability to learn from limited data and transfer knowledge from one field to another, these techniques have a high potential to improve diagnosis accuracy and treatment efficiency. However, using ML in diagnosing and treating psoriasis is associated with several challenges, including data limitations, the complexity of algorithms, and the need for high expertise to implement them properly. By presenting a detailed taxonomy, this article examines the applications and challenges of using ML techniques in psoriasis and analyzes the latest achievements in this field. The results of this study show that ML techniques have increased the accuracy of psoriasis diagnosis by 35% and improved treatment efficiency by 29%. In addition, these techniques reduced the data processing time by 21% and improved the overall treatment process. Also, these methods have increased the success rate of patient survival predictions by 15%. Finally, by examining the existing challenges and providing solutions to overcome these challenges, this research will help researchers and experts in this field develop new strategies to improve the diagnosis and treatment of psoriasis by better understanding ML applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11031-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictor-based neural network control for unmanned aerial vehicles with input quantization: design and application

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-12-20 DOI: 10.1007/s10462-024-11054-0

Di Wu

{"title":"Predictor-based neural network control for unmanned aerial vehicles with input quantization: design and application","authors":"Di Wu","doi":"10.1007/s10462-024-11054-0","DOIUrl":"10.1007/s10462-024-11054-0","url":null,"abstract":"<div><p>In this article, I design a predictor-based neural network (NN) controller for unmanned aerial vehicles (UAVs) with input quantization to address the trajectory tracking problem in the presence of time-varying disturbances caused by aerodynamics and external environment. The NN with a state predictor (SP) is employed in the controller design to improve transient performance without high-frequency oscillations and address the problem of instability caused by the time-varying disturbances. Additionally, the prediction errors from the SP are used to update the learning rate of the NN, resulting in smoother and faster learning responses. Furthermore, a hysteresis quantizer is employed to discretize signals and reduce the transmission burden on digital hardware, which can enhance the suitability of the system for practical implementation. Based on the Lyapunov method, the closed-loop system of the UAV achieves input-to-state stability (ISS). Finally, to validate and assess the performance and effectiveness of our proposed control method, I present and analyze both simulation results and experimental results from real-world applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11054-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0