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检索条件"主题词=PSO Algorithm"

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Intelligent Energy Management System for Hybrid Electric Vehicle Based on Optimization Wavelet Neural Network by pso algorithm 12th

Intelligent Energy Management System for Hybrid Electric Veh...

引用

12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022

作者： Kamoona, Mustafa Kivanc, Omer Cihan Ahmed, O.A. Department of Power Electronics and Clean Energy Systems Istanbul Okan University Istanbul34959 Turkey Department of Electrical and Electronics Engineering Istanbul Okan University Istanbul34959 Turkey Department of Electrical Engineering University of Technology Baghdad Iraq

ISBN: (纸本)9783031253430

An intelligent controller is proposed in this work for plug-in hydrogen Fuel Cell Hybrid Electric Vehicle (FCHEV) that integrated Fuel Cell (FC), Battery (BAT), and Ultracapacitor (UC) to reach a high dynamic response and keep high efficiency of energy storage resources. That controller manages the power flow of the system in an intelligent tracking to be optimal for FCHEV. Where the Fuzzy Logic Controller (FLC) and the Artificial Neural Networks (ANNs) are employed to meet the Energy Management System (EMS) requirements thereby efficient management has been developed for ensuring that three power sources are running at high efficiency with their mechanism performance and meets efficiently the load power demands and uses less hydrogen consumption. Moreover, the control strategy of the Wavelet Neural Network that is linked with the PI controller, named WNN-PI, and the parameters of WNN-PI are tuned by using the Particle Swarm Optimization (pso) algorithm is also used. Considering the battery and ultra-capacitor state-of-charge (SOC) with power conditioning unit converters that control the FC and BAT output and provide the desired voltage of the DC bus as well as keep the voltage stable at 300 V. Analysis and evaluations of the system have been done by MATLAB/Simulink environment while various vehicle driving cycles have been applied by using ADVISOR Simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial Neural Networks EMS Fuzzy Logic Controller pso algorithm Wavelet Neural Network

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A High_Performance Steganographic Method using JPEG and pso algorithm

A High_Performance Steganographic Method using JPEG and PSO ...

引用

12th IEEE International Multitopic Conference

作者： Fazli, Saeid Kiamini, Majid Zanjan Univ Dept Elect Eng Fac Zanjan Iran

ISBN: (纸本)9781424428236

In this paper, we present a novel method to embed secret message in the cover-image so that the interceptors will not notice about the existence of the hidden data. The basic concept of the proposed method is by simple Least Significant Bit (LSB) substitution. In order to improve the quality of stego-image and to increase the secret message capacity and security level, we inspire by the work of Li and Wang [1] which splits the cover-image into n blocks of 8 x 8 pixels and the secret message into n partitions. Then we apply Particle Swarm Optimization (pso) algorithm to search approximate optimal solutions and to rind an optimal substitution matrix for transforming the secret message in each block, instead of finding only one optimal substitution matrix for the whole cover-image as in [1]. The quality of the resulting stego-image, the secret message capacity and the security level of the proposed method are calculated and compared to other methods. Experimental results show that the proposed method outperforms the JPEG and Quantization Table Modification (JQTM) method and the Li and Wang's work in image quality, embedding capacity and security level.

关键词： Steganography Informtion hidding Cover-image Steg-image JPEG pso algorithm

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Study of Fault Feature Extraction Based on KPCA Optimized by pso algorithm

Study of Fault Feature Extraction Based on KPCA Optimized by...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Pan Hongxia Wei Xiuye Huang Jinying North Univ China Sch Mech Engn & Automat Taiyuan 030051 CO Peoples R China North University China Sch Mech Engn Taiyuan 030051 CO Peoples R China

ISBN: (纸本)9781424469208

For blindness of the parameter settings in kernel principal component analysis (KPCA), kernel function parameter optimized by particle swarm optimization (pso) algorithm is proposed, and KPCA is applied to feature extraction in fault diagnosis. The mathematical model of kernel function parameter optimized is constructed firstly, then the pso algorithm with adaptive accelerate (Cpso) is used to optimize it. The optimized KPCA is applied to feature extraction of gearbox typical faults. The results indicate that KPCA after parameter optimized can effectively reduce the dimensions of feature vector of gearbox, and it has a better fault classification performance than linear principal component analysis (PCA). This method has an advantage in nonlinear feature extraction of mechanical failure signal.

关键词： KPCA pso algorithm condition monitoring fault classification fault diagnosis fault diagnosis fault feature extraction feature extraction feature vector dimension gearbox gears kernel function parameter kernel principal component analysis linear principal component analysis mathematical model mechanical engineering computing mechanical failure signal nonlinear feature extraction particle swarm optimisation principal component analysis signal processing

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Modeling and Control of a Doubly Fed Induction Generator Using pso algorithm

Modeling and Control of a Doubly Fed Induction Generator Usi...

引用

12th IEEE International Multitopic Conference

作者： Jalilvand, Abolfazl Jabbari, Mehdi Govar, Gholamreza Zarei Khoshkhoo, Hamid Zanjan Univ Dept Elect Engn Zanjan Iran

ISBN: (纸本)9781424428236

This paper focused on modeling and controlling of a doubly-fed induction generator (DFIG). The presented model is developed based on the basic flux linkage, voltage and torque equations. In order to control the DFIG's active power, a suitable method based on pso algorithm has been proposed. The simulation results show that the presented method is a useful way to improve the output power of DFIG.

关键词： Doubly fed induction generat Output powe PI controller pso algorithm

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DESIGNING PID CONTROLLER FOR DC MOTOR SYSTEM BY MEANS OF ENHANCED pso algorithm WITH DISCRETE CHAOTIC LOZI MAP

DESIGNING PID CONTROLLER FOR DC MOTOR SYSTEM BY MEANS OF ENH...

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26th European Conference on Modelling and Simulation (ECMS 2012)

作者： Pluhacek, Michal Senkerik, Roman Davendra, Donald Zelinka, Ivan Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic VB TUO Dept Comp Sci Fac Elect Engn & Comp Sci Ostrava 70833 Czech Republic

ISBN: (纸本)9780956494443

The main aim of this paper is the utilization of discrete chaotic Lozi map based chaos number generator to enhance the performance of pso algorithm. This paper presents the results of research, in whichchaos enhanced pso algorithm is used to design an optimal PID controller for DC motor system. Obtained results are compared with other non-heuristic and heuristic methods.

关键词： PID controller Optimization Evolutionary algorithms pso algorithm Chaos Lozi map

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Network Comprehensive Optimization for Schedule and Cost in Project Construction Based on pso algorithm

Network Comprehensive Optimization for Schedule and Cost in ...

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IEEE International Conference on Automation and Logistics

作者： Huang, Yuansheng Qiu, Zilong Zhang, Weina N China Elect Power Univ Dept Econ & Management Baoding Hebei Province Peoples R China

ISBN: (纸本)9781424425020

Based on the principle of the maximum economic benefits can be obtained by optimizing the project network schedule, the comprehensive optimization of the cost, the supply of resources and the influence of latent uncertainties are considered systematically, and a mathematical model under multi-goal conditions is proposed in this paper. Then by integrating niche technology and using dynamic inertia weight, the particle swarm optimization algorithm is improved and is applied in the process of optimizing the model. Finally, an example is used to prove the improved pso algorithm is of high effectiveness in solving the comprehensive optimization of project schedule.

关键词： Network Comprehensive Optimization Schedule Control Cost Control pso algorithm

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Improvement of Grayscale Image Segmentation Based On pso algorithm

Improvement of Grayscale Image Segmentation Based On PSO Alg...

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4th International Conference on Computer Sciences and Convergence Information Technology

作者： Zheng, Liping Pan, Quanke Li, Guangyao Liang, Jing Liaocheng Univ Sch Comp Sci Liaocheng Shandong Peoples R China Tongji Univ CAD Res Ctr Shanghai Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China

ISBN: (纸本)9781424452446

Image segmentation is the base of image 3D reconstruction. It is a critical step in image processing. Threshold segmentation is a simple and important method in grayscale image segmentation. Maximum Entropy method is a common threshold segmentation method. This method only utilizes gray information. In order to adequately utilize spatial information of greyscale image, an improved 2D entropy segmentation method is proposed. This new method is called pso-SDAIVE algorithm. In this new method, the computation of 2D entropy is improved. Otherwise, Particle Swarm Optimization(pso) algorithm is used to solve maximum of improved entropy. Maximum takes as the optimal image segmentation threshold. In this paper, two head CT images are segmented in experiment. Compare with other segmentation method. Experimental results show that this new method can quickly and accurately obtain segmentation threshold. Otherwise, this method has strong anti-noise capability and saves computation time.

关键词： Grayscale Image Entropy Threshold Segmentation pso algorithm

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Mechanism Model of Pressurizer in the Pressurized Water Reactor Nuclear Power Plant Based on pso algorithm

Mechanism Model of Pressurizer in the Pressurized Water Reac...

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24th Chinese Control and Decision Conference (CCDC)

作者： Li Yongling Ma Jin Chan Afang Huang Yu Wang Bingshu North China Elect Power Univ Baoding 071003 Peoples R China

ISBN: (纸本)9781457720727

A pressurizer (PRZ) mechanism model was employed for pressurized water reactor (PWR) nuclear power plant to meet rapid development of its simulation requirements. According to PRZ operational characteristics, the laws of energy conservation, mass and momentum conservation are used in obtaining a dynamic two-phase imbalance mechanism model which considering the influence of the spray flow, the power of electric heater as well as factors related to the safety valves. Particle Swarm Optimization (pso) algorithm was proposed to obtain a set of optimized parameters in the PRZ mechanism model. The parameter optimization algorithm was applied to a simulation case in PRZ of a 900MW PWR nuclear power plant, and a pressure simulation model was created by means of module packaging. By comparing the simulation results with the datum obtained by the PWR nuclear power plant, the correctness of modeling and the effectiveness of optimization methods were verified.

关键词： Nuclear power Pressurizer Mechanism modeling Simulation pso algorithm

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Learning Interval-Valued Fuzzy Cognitive Maps with pso algorithm for Abnormal Stock Return Prediction 1

引用

6th International Conference on the Theory and Practice of Natural Computing (TPNC)

作者： Hajek, Petr Prochazka, Ondrej Univ Pardubice Fac Econ & Adm Inst Syst Engn & Informat Studentska 84 Pardubice 53210 Czech Republic

ISBN: (数字)9783319710693

ISBN: (纸本)9783319710693;9783319710686

Stock return prediction is considered a challenging task in financial domain. The existence of inherent noise and volatility in daily stock price returns requires a highly complex prediction system. Generalizations of fuzzy systems have shown promising results for this task owing to their ability to handle strong uncertainty in dynamic financial markets. Moreover, financial variables are usually in difficult to interpret causal relationships. To overcome these problems, here we propose an interval-valued fuzzy cognitive map with pso algorithm learning. This system is suitable for modelling complex nonlinear problems through causal reasoning. As the inputs of the system, we combine causally connected financial indicators and linguistic variables extracted from management discussion in annual reports. Here we show that the proposed method is effective for predicting abnormal stock return. In addition, we demonstrate that this method outperforms fuzzy cognitive maps and adaptive neuro-fuzzy rule-based systems with pso learning.

关键词： Stock market Interval-valued fuzzy cognitive map pso algorithm Abnormal stock return

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Parameter Group Optimization Strategy to Suppress Harmonic Current for PV-Storage Microgrid based on pso algorithm 9

Parameter Group Optimization Strategy to Suppress Harmonic C...

引用

IEEE 9th International Conference on Power Electronics Systems and Applications (PESA)

作者： Huang, Juntao Yang, Ling Zhu, Difan Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781665486682

In the photovoltaic (PV)-storage microgrid, the instantaneous power of single-phase inverter load contains a double frequency pulsating power component, which leads to the existence of second harmonic current in the input source. Therefore, this paper proposes a harmonic current suppression method based on notch filter and quasi-proportional resonant controller. Under this method, the inductor branch shows high impedance at double frequency and low impedance at non-double frequency. It can effectively suppress the second harmonic current while meeting the dynamic needs. Aiming at the problems of poor dynamic performance and reducing harmonic suppression ability caused by improperly selecting controller parameters, an optimization strategy for suppression parameter group based on particle swarm optimization (pso) is proposed. Since pso has the advantages of high accuracy and fast convergence speed, it is convenient to obtain the optimal suppression parameter group, which takes dynamic quality, steady state and harmonic suppression ability into account. The experimental results verify the effectiveness of proposed method and optimization strategy.

关键词： harmonic current suppression parameter group optimization PV-storage microgrid pso algorithm

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