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IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference

作者： Chen Ying-Xi Liao Xiao-Dong Wang Jian-Hua Tao Zhuang Sui Li-Yue College of Photonic and Electronic Engineering Fujian Normal University Fuzhou Fujian China Key Laboratory of Optoelectronic Science and Technology for Medicine Fujian Normal University Fuzhou Fujian China

With the development of information technology more and more rapidly, the development of image recognition classification technology is also increasingly improved. This paper proposes a method which combines random Dropout algorithm with pso-BP to realize the recognition and classification of small images. On the basis of the error back propagation adjustment weight of the traditional BP algorithm, the weight correction of pso algorithm is introduced, and the pso-BP network model is established. Then the hidden layer unit of pso-BP network is improved by using stochastic Dropout algorithm, so the fast operation speed is obtained. The results show that compared with the traditional BP and the pso-BP algorithm, the network error rate is obviously reduced after the algorithm is improved in this paper, and the real-time performance of the algorithm is improved to a certain extent.

关键词： Image identification Random dropout pso algorithm BP neural network

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A pso-based algorithm for optimal multiple chiller systems operation

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APPLIED THERMAL ENGINEERING 2012年第1期32卷 31-40页

作者： Beghi, Alessandro Cecchinato, Luca Cosi, Giovanni Rampazzo, Mirco Univ Padua Dipartimento Ingn Informaz I-35131 Padua Italy Univ Padua Dipartimento Fis Tecn I-35131 Padua Italy

In Heating, Ventilation and Air Conditioning (I-IVAC) plants of medium-high cooling capacity, multiple chiller systems are often employed. System performance is evaluated in terms of user comfort, energy use, and financial costs. The problem of efficiently managing multiple chiller systems is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. Therefore significant energy savings can be achieved by optimizing chiller operation. In this paper an unified method for efficient management of multiple chiller systems, by means of a Particle Swarm Optimization (pso) based algorithm, is presented. In particular, the results show that it is possible to achieve substantial energy savings while granting good load profile tracking with respect to standard approaches. The performance of the algorithm is evaluated by resorting to a simulation environment, where the plant dynamics are accurately described. The results show that pso satisfactorily deals with such kind of nonlinear constrained optimization problem, while offering advantages, with respect to other optimization techniques, in terms of ease of implementation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multiple chiller Optimal chiller loading Optimal chiller sequencing pso algorithm

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An ECM-pso based optimal charging current pattern searching for Li-ion batteries

An ECM-PSO based optimal charging current pattern searching ...

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2017 International Conference on Material Science,Energy and Environmental Engineering(MSEEE 2017)

作者： Haitao Min Weiyi Sun Zhiru Liu Yuanbin Yu Dongni Guo State Key Laboratory of Automotive Simulation and Control Jilin University

ISBN: (纸本)9781510847002

EV battery charging is growing in importance as it has a direct influence on the EV *** Constant Current(MCC) charging strategy is an effective rapid charging *** paper proposes an ECM-pso algorithm to search for the optimal current pattern of the MCC charging *** MCC charging model is built according to the Li-ion battery equivalent circuit model, and the object function for the ECM-pso optimization was formulated and the optimal current pattern of the MCC charging method was obtained through the ECM-pso *** experimental result shows that the obtained current pattern is capable of charging the batteries to 87.7% of the rated capacity within 36.5 *** with the conventional CC-CV charging method, the proposed charging strategy has a performance improvement of 60.3% in charging time reduction, and the average temperature rise during the proposed MCC charging process is 0.5 ℃ lower than the CC-CV charging method.

关键词： Li-ion batteries ECM pso algorithm Multistage constant current charging

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Design and Optimization of Hybrid Micro-Grid System

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Energy Procedia 2017年 117卷 95-103页

作者： M Jayachandran G Ravi

Hybrid microgrid systems (HMGS) comprise of several parallel connected distributed resources with electronically controlled strategies, which are capable to operate in both islanded and grid connected mode. HMGS based on renewable energy sources (RES) is the cost-effective option for solving the power supply problem in remote areas, which are located far from grids. In this paper, the wind and solar meteorological data for Sundarban (India) station are used to design Islanded HMGS for providing necessary electricity. Cost effectiveness and system reliability are major factors considered for designing HMGS to achieve better power management scheme. Particle Swarm Optimization (pso) scheme is applied to identify the sizing of wind turbines (WT), photovoltaic (PV) module, battery energy storage system (BESS) and diesel generator, and find the optimal configuration of HMGS system. The design and optimal operation of HMGS system has been developed and validated through MATLAB software.

关键词： HMGS pso algorithm Power Management Scheme

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Research on capacity optimization of PV-wind-diesel-battery hybrid generation system

Research on capacity optimization of PV-wind-diesel-battery ...

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International Power Electronics Conference

作者： Cailing Zhu Furong Liu Sheng Hu Shu Liu College of Automation Wuhan university of technology Wuhan China SINOMACH Intelligence Technology Research Institute Wuhan China

For small scale isolated PV-wind-diesel-battery hybrid generation system, the capacity optimization needs to determine capacity of each component. This paper proposes an optimization method that takes economic and technical indexes into account, which optimize the capacity of the battery and diesel generators under the premise of satisfying the technical indexes, and also optimize capacity of wind turbine (WT) and photovoltaic array (PV) by considering the economic index. Among them, the economic index of the system is to minimize the life-cycle cost (LCC). According to the wind-solar resources of the installed site, the mathematical model of the system is built after analyzing the output characteristics of each micro-source and the energy dispatching strategy. Finally, the particle swarm optimization (pso) algorithm is adopted to obtain the minimum cost and the optimal capacity configuration scheme by MATLAB simulation software. The influence of capacity of WT and PV on LCC is analyzed too.

关键词： PV-wind-diesel-battery system Economic and technical indexes Capacity optimization pso algorithm

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Dynamic modeling and control of IPMC hydrodynamic propulsor

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SMART STRUCTURES AND SYSTEMS 2017年第4期20卷 499-508页

作者： Agrahari, Shivendra K. Mukherjee, Sujoy PDPM Indian Inst Informat & Technol Design & Mfg Jabalpur 482005 Madhya Pradesh India

The ionic polymer metal composite (IPMC) is an electroactive polymer material and has a promising potential as actuators for propulsion and locomotion in underwater systems. In this paper a physics based model is used to analyse the actuation dynamics of the IPMC propulsor. Moreover, proportional-integral (PI) controller is used for position control of the tip displacement of IPMC propulsor. PI parameter tuning is performed using particle swarm optimization (pso) algorithm. Several performance indices have been used as an objective function to optimize the error of the system. Finally, the best tuning method is found out by comparing the results under various performance indices.

关键词： Ionic Polymer Metal Composite (IPMC) smart materials propulsor PI controller pso algorithm

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Power System Oscillations Damping by Optimal Coordinated Design Between PSS and STATCOM Using pso and ABC algorithms 13

Power System Oscillations Damping by Optimal Coordinated Des...

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13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Shahgholian, Ghazanfar Fazeli-Nejad, Saeid Moazzami, Majid Mahdavian, Mehdi Azadeh, Manijeh Janghorbani, Mohammadreza Farazpey, Saeed Islamic Azad Univ Najafabad Branch Dept Elect Engn Esfahan Iran Islamic Azad Univ Naein Branch Dept Elect Engn Naein Isfahan Iran Islamic Azad Univ Cent Tehran Branch Young Researchers & Elite Club Tehran Iran

ISBN: (纸本)9781467397490

Coordinated design of the power system stabilizer (PSS) and static synchronous compensator controller parameters (STATCOM) using artificial bee colony algorithm (ABC) to improve power system stability as an optimizations problem has been proposed in this study. ABC algorithm is a collective inteligence based on optimization algorithm inspired by the feeding behavior of bees in finding food. Rapid convergence and high precision are the capabilities of this algorithm which in this study to demonstrate its effectiveness and robustness, a two zone - four machines system is used and analyzed through non-linear simulation of time-domain and the results are compared with particle swarm optimization (pso) algorithm. The optimization after a large turbulence shows that the coordinated design of the STATCOM and PSS controllers' parameters using the ABC algorithm considerably improves the system stability together with rapid damping of the system fluctuations as compared to the state without optimization. On the other hand, the optimization results using the ABC algorithm shows the superiority of this method over the pso algorithm.

关键词： PSS STATCOM ABC algorithm pso algorithm transient stability

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An Improved pso-based algorithm for Extracting Week Coupling AC Signal in Electromagnetic Localization System

An Improved PSO-based Algorithm for Extracting Week Coupling...

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IEEE International Conference on Automation and Logistics (ICAL)

作者： Wang, Wenhu Hu, Chao Lin, Weixing Bao, Jianmeng Zhejiang Univ Ningbo Inst Technol Ningbo 315100 Zhejiang Peoples R China Ningbo Univ Ningbo 315211 Peoples R China

ISBN: (纸本)9781467303644

The AC electromagnetic localization method is often used to locate the position of the objective such as the moving robot or vehicle. This technique must extract the coupling magnetic signals between the source and the receiving magnetic coils, because the localization algorithm determines objective's position and orientation by the intensities or the phases of the coupling signals. This extraction must be accurate to ensure the localization precision. However, these couplings voltage signals are very week and easily affected by interferences, so high efficient and robust extraction method is essential. In this paper, we present rapid particle swarm optimization (pso) algorithm to accurately extract this coupling signal. Through the simulation and serious comparisons, we found that pso algorithm has higher accuracy and faster speed than the least square (LS) method, especially in cases with high noises. We show the simulation results that are satisfactory to extract the coupling voltage signals.

关键词： Magnetic Localization AC Signal Extraction pso algorithm

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Ship Hull Optimization Based on New Neural Network

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电脑学刊 2017年第1期28卷 137-148页

作者： Yuan-Hang Hou Xiao-Jing Jiang Xiong-Hua Shi

Pointing at optimization design of hull form based on SBD (simulation based design) technology, a new neural network approximation technique is proposed. First, through using pso (particle swarm optimization) algorithm training FRBF (flexible radial basis function) neural network weights, pso-FRBF neural network algorithm is proposed. By comparison and analysis of the wave resistance coefficient of different methods, applicability and superiority of the new algorithm is proved. Then, Wigley hull is taken as example, with the principal dimensions and parameters as design variables, and variation of displacement as constraint condition, the total resistance optimization model is established through introducing pso-FRBF wave resistance coefficient approximation model. Then the simulated annealing algorithm is used in the ship hull optimal design, and a reliable and reasonable optimized ship hull is obtained. The new neural network can provide fine technical support for related ship optimization design stage.

关键词： approximate accuracy FRBF neural network hull form optimization pso algorithm

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A pso-based Weighting Method to Enhance Machine Learning Techniques for Cooperative Spectrum Sensing in CR Networks 1

A PSO-based Weighting Method to Enhance Machine Learning Tec...

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1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Ghazizadeh, Elham Nikpour, Bahareh Moghadam, Dariush Abbasi Nezaniabadi-pour, Hossein Shahid Bahnoar Univ Kerman Kerman Province Iran

ISBN: (纸本)9781467387378

Cognitive radio (CR) is a recent technology to tackle the problem of radio spectrum scarcity. Successful spectrum sensing is fundamental in performance of CR networks;hence, a pso-based weighting method is proposed in order to improve the functionality of machine learning techniques which are used with the aim of detecting the activity of secondary users in cooperative cognitive radio (CCR) networks. Regarding classification methods, three supervised classifiers which are supported vector machines (SVM), K-nearest neighbors (K-NN) and naive Bayes are used for pattern classification. Since our goal is spectrum sensing in CCR networks, the vector of energy levels in radio channel which is considered as a feature vector is fed into the classifier to determine the availability of the channel. The classifier labels each feature vector as two classes: the "channel available class" or the "channel unavailable class". In our proposed method, first, the three mentioned classifiers go through a training phase. Next, for new feature vectors, a label is assigned to the feature vector by each classifier and the final decision about the availability of the channel is made by a weighted voting method based on the pso algorithm in an online fashion. The performance of our technique is measured in terms of the classification error. Also, the comparative results show twofold merit over previous methods since it not only reduces the error rate but also decreases the error of the channel available class.

关键词： cognitive radio cooperative spectrum sensing K-NN Naive Bayes SVM pso algorithm

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