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    Wyświetlanie 1-7 z 7
    Tytuł:
    Fuzzy synergetic control for dynamic car-like mobile robot
    Autorzy:
    Bouhamatou, Zoulikha
    Abedssemed, Foudil
    Powiązania:
    https://bibliotekanauki.pl/articles/2106222.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    CLMR
    synergetic control
    lyapunov stability
    GWO
    fuzzy logic type 2
    Opis:
    This paper aims to present the dynamic control of a Car-like Mobile Robot (CLMR) using Synergetic Control (SC). The SC control is used to make the linear velocity and steering velocity converge to references. Lyapunov synthesis is adopted to assure controlled system stability. To find the optimised parameters of the SC, the grey wolf optimiser (GWO) algorithm is used. These parameters depend on the best-selected fitness function. Four fitness functions are selected for this purpose, which is based on the integral of the error square (ISE), the integral of the square of the time-weighted error (ITSE), the integral of the error absolute (IAE) and the integral of the absolute of the time-weighted error (TIAE) criterion. To go further in the investigation, fuzzy logic type 2 is used to get at each iteration the appropri-ate controller parameters that give the best performances and robustness. Simulations results are conducted to show the feasibility and efficiency of the proposed control methods.
    Źródło:
    Acta Mechanica et Automatica; 2022, 16, 1; 48--57
    1898-4088
    2300-5319
    Pojawia się w:
    Acta Mechanica et Automatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks
    Autorzy:
    Doorwar, Minaxi
    Malathi, P
    Powiązania:
    https://bibliotekanauki.pl/articles/27311958.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    multimedia
    network
    Q-learning
    GWO
    GA
    Adhoc
    QoS
    iterative
    process
    Opis:
    Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for largescale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 4; 776--784
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
    Autorzy:
    He, Ping
    Dong, Jie
    Wu, Xiaopeng
    Yun, Lei
    Yang, Hua
    Powiązania:
    https://bibliotekanauki.pl/articles/27309934.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    BP neural network
    photovoltaic power generation
    PSO–GWO model
    PSO–GWO–BP prediction model
    particle swarm optimization
    gray wolf optimization
    back propagation
    standard grey wolf algorithm
    Opis:
    At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 3; 613--628
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of fractional order PI controller using metaheuristics algorithms applied to multilevel inverter for grid connected PV
    Autorzy:
    Boucheriette, Wafa
    Mechgoug, Raihane
    Benguesmia, Hani
    Powiązania:
    https://bibliotekanauki.pl/articles/11541888.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    MLI inverter
    fractional order PI
    meta-heuristic
    PSO
    ABC
    GWO
    THD
    metaheurystyka
    falownik
    układ regulacji
    Opis:
    Due to its multiple advantages in industrial and grid-connected applications, Multi-Level Inverters (MLIs) have increased in popularity in recent years. To improve the efficiency of a grid-connected PV system's integrated multi-level inverter fractional order PI (FOPI) controllers are used to describe the control process. The control system is made up of three control loops based on FOPI controllers: one for controlling the intermediate circuit voltage (Vdc) and the other two for controlling the direct and quadratic currents (Id, Iq) supplied by the multilevel inverter. The proposed controller parameters (Kp, KI, λ) must be selected in order to increase the efficiency of the multi-level inverter while decreasing the total harmonic distortion (THD) of the output current of the inverter as well as voltage. For this we used three meta-heuristic algorithms (PSO, ABC, GWO). The performance of the three controllers PSO-FOPI, ABC-FOPI and GWO-FOPI controller is compared. The findings showed that GWO-FOPI performs better than the other PSO-FOPI and ABC-FOPI in accuracy and total harmonic distortion THD term. The simulation will be conducted using Matlab/Simulink.
    Źródło:
    Diagnostyka; 2023, 24, 3; art. no. 2023311
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of the Grey Wolf Optimizer in the optimization of state space controller for three-mass drive
    Autorzy:
    Żychlewicz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1189934.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    GWO
    regulator stanu
    układ trójmasowy
    napęd elektryczny
    Grey Wolf Optimizer
    state space controller
    three-mass system
    electrical drive
    Opis:
    This article presents control structure of complex drive that contains two elastic couplings. In the outer control loop the state space controller was implemented. The main point of described work is optimization of parameters used in this part of the drive using metaheuristic algorithm called GWO (Grey Wolf Optimizer). The control structure, designed using mentioned optimization method, was compared to classic solution, known from control theory. High precision of reference speed tracking was achieved. An analysis of the system in the presence of mechanical parameters changes was also prepared. Theoretical considerations were confirmed in numerical tests.
    Źródło:
    Interdisciplinary Journal of Engineering Sciences; 2018, 6, 1; 12--20
    2300-5874
    Pojawia się w:
    Interdisciplinary Journal of Engineering Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Global path planning for multiple AUVs using GWO
    Autorzy:
    Panda, Madhusmita
    Das, Bikramaditya
    Pati, Bibhuti
    Powiązania:
    https://bibliotekanauki.pl/articles/229749.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Autonomous Underwater Vehicle
    AUV
    Genetic Algorithm
    GA
    Global Path Planning
    GPP
    Grey Wolf Optimization
    GWO
    Sliding Mode Control
    SMC
    waypoints
    Opis:
    In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.
    Źródło:
    Archives of Control Sciences; 2020, 30, 1; 77-100
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Feature optimization using a two-tier hybrid optimizer in an Internet of Things network
    Autorzy:
    Agrawal, Akhileshwar Prasad
    Singh, Nanhay
    Powiązania:
    https://bibliotekanauki.pl/articles/15548024.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    IoT
    Internet of Things
    anomaly mitigation
    GWO
    Gray Wolf Optimizer
    feature optimization
    PSO
    particle swarm optimizer
    Internet Rzeczy
    optymalizacja funkcji
    Opis:
    The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 2; 313--326
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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