Fractional-Order Controller for the Course Tracking of Underactuated Surface Vessels Based on Dynamic Neural Fuzzy Model DOI Creative Commons
Guangyu Li, Yanxin Li, Xiang Li

и другие.

Fractal and Fractional, Год журнала: 2024, Номер 8(12), С. 720 - 720

Опубликована: Дек. 5, 2024

Aiming at the uncertainty problem caused by time-varying modeling parameters associated with ship speed in course tracking control of underactuated surface vessels (USVs), this paper proposes a algorithm based on dynamic neural fuzzy model (DNFM). The DNFM simultaneously adjusts structure and during learning fully approximates inverse dynamics ships. Online identification lays foundation for motion control. trained DNFM, serving as an controller, is connected parallel fractional-order PIλDμ controller to be used ship’s course. Moreover, weights can further adjusted tracking. Taking actual data 5446 TEU large container ship, simulation experiments are conducted, respectively, tracking, under wind wave interferences, comparison five different controllers. This proposed overcome influence parameters, desired quickly effectively.

Язык: Английский

Study on a new metaheuristic algorithm – Tribal intelligent evolution optimization and its application in optimal control of cooling plants DOI
Ye Yao,

Xiaoxi Hong,

Lei Xiong

и другие.

Applied Energy, Год журнала: 2025, Номер 383, С. 125339 - 125339

Опубликована: Янв. 15, 2025

Язык: Английский

Процитировано

3

Multi-strategy ant colony optimization with k-means clustering algorithm for capacitated vehicle routing problem DOI
Zhaojun Zhang,

Simeng Tan,

Jia-Jia Qin

и другие.

Cluster Computing, Год журнала: 2025, Номер 28(3)

Опубликована: Янв. 21, 2025

Язык: Английский

Процитировано

0

Bearing fault feature extraction method for aperiodic stationarity caused by strong noise background DOI
Fengqi Zhou, Fengxing Zhou, Baokang Yan

и другие.

Tribology Transactions, Год журнала: 2025, Номер unknown, С. 1 - 21

Опубликована: Янв. 30, 2025

1. Rolling element bearings are the critical parts of every rotating machinery and their failure is one main reasons machine downtime even breakdown. The significance early detection cannot be overstated, as it plays a crucial role in maintaining proper functioning equipment, enhancing production efficiency, ensuring safety. Among them, envelope analysis most effective widely used approach, working according to principle linear filtering process signals remove undesirable components. However, characteristic frequencies can no longer evident or overwhelmed due weak signal. We propose an fault feature extraction method that combines correlation entropy with improved 2.5-dimensional square spectrum. This approach designed overcome relatively impact failures, which easily obscured by external background noise. Specifically, matrix simplified into series entropies, formula for higher-order spectrum enhanced achieve superior noise removal performance. Furthermore, enhance frequency (FCF), Fourier transform has been refined transform. Simulation results demonstrate our proposed excels at extracting bearing characteristics detecting inner outer ring FCFs, thereby offering practical value.

Язык: Английский

Процитировано

0

Advanced deep learning model for direct phase-only hologram generation using complex-valued neural networks DOI
Shariar Md Imtiaz, Tuvshinjargal Amgalan, Ferdous Hossain

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129672 - 129672

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Data Informed Initialization of Fuzzy Membership Functions DOI Creative Commons
Tao Wang, Richard Gault, Des Greer

и другие.

International Journal of Fuzzy Systems, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

0

Application of a spherical fuzzy entropy measure in the selection of the best hazardous waste transportation firm DOI Open Access
Abdul Haseeb Ganie, Debashis Dutta

Journal of Industrial and Management Optimization, Год журнала: 2025, Номер 0(0), С. 0 - 0

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

A multiple level competitive swarm optimizer based on dual evaluation criteria and global optimization for large-scale optimization problem DOI
Chen Huang, Yingjie Song,

Hongjiang Ma

и другие.

Information Sciences, Год журнала: 2025, Номер 708, С. 122068 - 122068

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0

Improvement strategies for heuristic algorithms based on machine learning and information concepts: a review of the seahorse optimization algorithm DOI Creative Commons
Shixin Zheng

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2805 - e2805

Опубликована: Апрель 1, 2025

To overcome the mechanical limitations of traditional inertia weight optimization methods, this study draws inspiration from machine learning models and proposes an strategy based on K-nearest neighbors (KNN) principle with dynamic adjustment properties. Unlike conventional approaches that determine solely number iterations, proposed allows to more accurately reflect relative distance between individuals target value. Consequently, it transforms discrete “iteration-weight” mapping ($t\rightarrow w$) into a continuous “distance-weight” ($d\rightarrow w$), thereby enhancing adaptability capability algorithm. Furthermore, inspired by entropy method, introduces entropy-based allocation mechanism in crossover mutation process improve efficiency high-quality information inheritance. validate its effectiveness, is incorporated Seahorse Optimization Algorithm (SHO) systematically evaluated using 31 benchmark functions CEC2005 CEC2021 test suites. Experimental results demonstrate improved SHO algorithm, integrating logistic-KNN crossover-mutation mechanism, exhibits significant advantages terms convergence speed, solution accuracy, algorithm stability. further investigate performance improvements, conducts ablation experiments analyze each modification separately. The confirm individual significantly enhances overall

Язык: Английский

Процитировано

0

Patrones de Comportamiento en usuarios de transporte interprovincial en Ecuador mediante Técnicas de Machine Learning DOI Creative Commons
G Aguilar, José Fernando López Aguirre, Juan Carlos Pomaquero Yuquilema

и другие.

Revista Venezolana de Gerencia, Год журнала: 2025, Номер 30(110), С. 1047 - 1061

Опубликована: Апрель 4, 2025

Este estudio tiene como objetivo analizar y predecir patrones de comportamiento los usuarios transporte interprovincial en Ecuador mediante técnicas aprendizaje automático. Se utilizó un conjunto datos proporcionado por la Unión Cooperativas Transporte Interprovincial que abarca viajes realizados entre 2022 2024. La metodología incluyó implementación K-means para segmentación PCA reducción dimensional. Inicialmente, identificó cuatro clústeres, pero el solapamiento grupos motivó aplicación PCA, mejorando separación. Los resultados revelaron grupos: Ritmo Diario, Exploradores Fin Semana, Nómadas Eventos Viajeros Flexibles. Esta ofrece información clave optimizar servicios mejorar experiencia del usuario al ajustar recursos a las necesidades cada grupo.

Процитировано

0

A diversity enhanced tree-seed algorithm based on double search with genetic and automated learning search strategies for image segmentation DOI
Xianqiu Meng, Gaochao Xu, Xu Xu

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113143 - 113143

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0