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检索条件"主题词=Particle swarm optimization algorithm"

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Short-Term Traffic Flow Prediction Method Based on Grey Adaptive particle swarm Support Vector Machine 2

Short-Term Traffic Flow Prediction Method Based on Grey Adap...

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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Duo, Mei Xu, E. Qi, Yan Bohai Univ Coll Informat Sci & Technol Jinzhou Peoples R China

ISBN: (纸本)9781467390262

In order to further improve the accuracy of the short-term traffic flow prediction, a combination of short-term traffic flow prediction model had been proposed by analyzing the characteristics of grey model, adaptive particle swarm optimization (PSO) algorithm and support vector machine (SVM) model. First, use the grey model to accumulate the original traffic flow data, weaken the randomness of the traffic flow data sequence, then optimize the support vector machine model based on adaptive particle swarm optimization algorithm and realize short-term traffic flow prediction, finally, get the final predicted value table by grey mode. The model was verified based on the traffic flow data of the major road in Changchun and the experimental result showed the proposed model was effective and feasible.

关键词： traffic flow prediction grey model particle swarm optimization algorithm support vector machine

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Structural Design of Optimized Polynomial Radial Basis Function Neural Networks

Structural Design of Optimized Polynomial Radial Basis Funct...

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7th International Symposium on Neural Networks

作者： Kim, Young-Hoon Kim, Hyun-Ki Oh, Sung-Kwun Univ Suwon Dept Elect Engn Hwaseong Si 445743 Gyeonggi Do South Korea

ISBN: (纸本)9783642132773

In this paper, we introduce optimization methods of Polynomial Radial Basis Function Neural Network (pRBFNN). The connection weight of proposed pRBFNN is represented as four kinds of polynomials, unlike in most conventional RBFNN constructed with constant as connection weight. Input space in partitioned with the aid of kernel functions and each kernel function is used Gaussian type. Least Square Estimation (LSE) is used to estimate the coefficients of polynomial. Also, in order to design the optimized pRBFNN model, center value of each kernel function is determined based on C-Means clustering algorithm, the width of the RBF, the polynomial type in the each node, input variables are identified through particle swarm optimization (PSO) algorithm. The performances of the NOx emission process of gas turbine power plant data and Automobile Miles per Gallon (MPG) data was applied to evaluate proposed model. We analyzed approximation and generalization of model.

关键词： Polynomial Radial Basis Function Neural Networks (pRBFNN) C-Means particle swarm optimization algorithm Machine Learning data

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Optimal location and sizing of STATCOM using Fuzzy-PSO approach 6

Optimal location and sizing of STATCOM using Fuzzy-PSO appro...

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6th IEEE International Conference on Power Systems (ICPS)

作者： Rana, Bharat Singh Srivastava, Laxmi Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

ISBN: (纸本)9781509001286

In this paper, an evolutionary computing technique comprising of Fuzzy logic and particle swarm optimization (PSO) algorithm has been proposed for optimal location and sizing of STATCOM to improve the voltage stability and to minimize the total voltage deviation in a power system. The proposed Fuzzy-PSO algorithm has been applied using two steps. As the first step, the weakest buses selected for placement of STATCOM using modal analysis. In the second step Fuzzy-PSO algorithm has been applied for selecting optimal location of STATCOM considering these selected weakest buses. The proposed approach has been applied on the standard IEEE 30-bus system considering the most severe single line outage contingencies under normal loading and under stressed condition. The proposed Fuzzy-PSO approach has been found to be quite satisfactory.

关键词： fuzzy performance index modal analysis particle swarm optimization algorithm STATCOM voltage stability

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Adaptive PMD compensation up to second-order in 40Gb/s OTDM optical communication system using two-stage compensator

Adaptive PMD compensation up to second-order in 40Gb/s OTDM ...

引用

Conference on Optical Transmission, Switching and Subsystems II

作者： Zhang, XG Xi, LX Yu, L Zhou, GT Zhang, JZ Zhang, N Wu, B Yuan, TC Chen, L Zhang, HM Yao, MY Yang, BJ Beijing Univ Posts & Telecommun Sch Sci Dept Phys Beijing 100876 Peoples R China

ISBN: (纸本)0819455792

In this paper we report a successful experiment of adaptive polarization mode dispersion (PMD) compensation up to second-order in a 40Gb/s optical time-division multiplexed (OTDM) communication system by using two-stage compensator. In the experiment the PMD monitoring technique based on degree of polarization (DOP) was adopted. And the particle swarm optimization (PSO) algorithm was introduced in adaptive PMD compensation, with the desirable features of fast convergence to the global optimum point for compensation without being trapped in local sub-optima and with good robustness to noise. The comparison was made to estimate the performance effectiveness between PSO algorithms with global neighborhood structure (GPSO) and with local neighborhood structure (LPSO). The LPSO algorithm is shown to be more effective to search global optimum for PMD compensation than GPSO algorithm. The ability of tracking changed PMD using PSO algorithm was also investigated. The two-stage PMD compensator is shown to be effective for both first- and second-order PMD, and the compensator is shown to be bit rate independent. The compensation time is within several hundreds of milliseconds. The response time for recovery from a sharp disturbance is about 11 ms.

关键词： polarization mode dispersion adaptive compensation particle swarm optimization algorithm

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A Novel Model Based on PSO-GA-RBF Approach for Predicting Roll Gap During Unsteady Processes 3

A Novel Model Based on PSO-GA-RBF Approach for Predicting Ro...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Zhang, Ruixiao Mao, Chaoxing Hu, Jinkai Liu, Zhao Guo, Hengda North China University of Science and Technology Tangshan China

ISBN: (纸本)9798350380989

To enhance the control accuracy of strip thickness in the non-stationary process of cold continuous rolling, a PSO-GA-RBF based prediction model for the non-stationary roll gap is proposed. This model combines big data algorithms with actual production data, including roll gap, rolling force, and rolling speed. A 1450mm series cold rolling production line is selected as the research object. Pearson analysis is utilized to perform a correlation analysis on the original data indicators, while principal component analysis is employed to reduce the dimensionality of the sample data. The optimization process of particle swarm optimization is enhanced by introducing crossover and mutation operators from genetic algorithms. This optimization aims to improve the parameter selection mechanism of the radial basis function neural network, thereby enhancing the model's prediction accuracy and generalization performance. The performance of different prediction models is analyzed, comparing them based on evaluation indicators such as mean square error, average absolute error, and average absolute error percentage. The results indicate that the PSO-GA-RBF based roll gap prediction model demonstrates good predictive ability and generalization performance. To further validate the effectiveness of the prediction model, a comparison is drawn between the prediction model and the roll gap data obtained from engineering practice. The experimental results show a close correspondence in trend and period between the two, verifying the strong prediction performance of the roll gap prediction model. The validated model offers benefits such as reducing the workload of the automatic control system and improving the control accuracy of strip steel thickness. © 2024 IEEE.

关键词： Genetic algorithm particle swarm optimization algorithm Radial basis function neural network Roll gap model Thickness control

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A Fuzzy Multi-Objective optimization Method Solving the Output of Energy Storage System 6

A Fuzzy Multi-Objective Optimization Method Solving the Outp...

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6th International Symposium on Parallel Architectures, algorithms, and Programming (PAAP)

作者： Wang, Yiyi Huang, Mei Zhang, Caiping Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr NANTEC Beijing Peoples R China

ISBN: (纸本)9781479938445

The electricity consumption of a charging station is obviously fluctuant. An energy storage system is an effective device to improve the load variation of the charging station. In order to give full play to energy storage system, this paper proposes a charging and discharging control strategy of the storage system considering multi-objective including stabilizing load and economizing electric consumption, changing a bi-objective problem into a single objective problem with weights after fuzzy processing. This paper solves the problem based on particle swarm optimization algorithm, and considers constraints including the demand of uninterruptible power supply and SOC at given time, which has greater practical significance. In this paper, the data of a practical integrated smart grid construction project is taken into considered. Finally, we verify the effectiveness and feasibility of the optimization strategy after simulation analysis.

关键词： energy storage system charging scheduling electric vehicles particle swarm optimization algorithm multi-objective optimization

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Improved Power Sharing with a Back-to-Back Converter and State-Feedback Control in a Utility-Connected Microgrid 2

Improved Power Sharing with a Back-to-Back Converter and Sta...

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2nd IEEE Texas Power and Energy Conference (TPEC)

作者： Jafari, Hassan Mahmoudi, Maryam Fatehi, Alireza Naderi, Mohammad H. Kaya, Elif Amirkabir Univ Technol Elect Engn Dept Tehran Iran KN Toosi Univ Technol Elect Engn Dept Tehran Iran Texas A&M Univ Elect Engn Dept College Stn TX USA

ISBN: (纸本)9781538610060

This study puts forward a strategy for optimal power sharing control in a microgrid that is connected to a utility grid through a back-to-back (B2B) converter. In grid-connected mode, the B2B converter totally isolates the microgrid from the utility grid in terms of voltage and frequency. In the proposed strategy, a pre-specified amount of power exchanged between the utility grid and the microgrid is regulated via active/reactive control in the B2B converter. This regulation means that distributed generation (DG) units supply the rest of microgrid load demand and track load changes through droop control. In both the voltage source inverter (VSI) of the B2B converter and the DG units, state-feedback control is employed as an inner control loop for tracking the state variable reference signals generated by the corresponding outer loops of the variables. Microgrid stability is essential and highly affected by controller parameters, droop coefficients, and the components of inductor-capacitor-inductor filters. In this regard, control is formulated as an optimization problem, for which particle swarm optimization is used to optimally calculate the parameters of the system and the controllers. Objective functions are derived by minimizing an eigenvalue-based function. The PSCAD simulation results demonstrate the effectiveness of the proposed control strategy.

关键词： Back-to-back converter particle swarm optimization algorithm grid-connected mode angle droop power sharing state-feedback control

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Optimal Placement of Fault Indicators in Distribution System Using PSO algorithm 43

Optimal Placement of Fault Indicators in Distribution System...

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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Li, Weijian Chen, Wei Guo, Chuangxin Jin, Yu Gong, Xiao Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China China Southern Power Grid Guizhou Power Grid Guiyang Power Supply Bur Guiyang Guizhou Peoples R China

ISBN: (纸本)9781538611272

Recently, fault indicators with communication function have been increasingly used in fault diagnosis of distribution networks. In this paper, an objective function which considers the reliability index of the distribution network and economic factors synthetically has been proposed for optimal placement of fault indicators in distribution networks, and binary particle swarm optimization algorithm is used for solving the optimization problem. The validity of the proposed model is verified by testing on the IEEE-33 node distribution system. The test results show that there is no need to install too many fault indicators in distribution networks and reasonable choice of the number and location of the fault indicator can achieve the comprehensive optimization of distribution network reliability and economy. At the same time, the results and processes of optimization are compared with those obtained by immune algorithm, the result shows that binary particle swarm optimization algorithm is better because of its fast convergence speed and less iteration times, which is an advantage for solving this optimal allocation problem.

关键词： Fault Indicators Optimal Placement particle swarm optimization algorithm

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Optimal Design of Fractional Order Fuzzy PID Controller with Simultaneous Auto-tuned Fuzzy Control Rules and Membership Functions 2

Optimal Design of Fractional Order Fuzzy PID Controller with...

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2nd Conference on swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Chaleshtori, Hengameh Nouraei Mohammadi, Seyed Mohammad Ali Bijami, Ehsan Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9781509043309

This paper proposes a new intelligent particle swarm optimization (PSO) based method for design of optimal fractional order fuzzy PID (FOFPID) controller with simultaneous auto-tuned fuzzy control rules and membership functions. In the proposed method the parameters of FOFPID controller including input scaling factors, output scaling factors, fractional order of derivative and integrator, fuzzy rule base and membership functions are considered as tuning parameters and optimized simultaneously using PSO algorithm. Moreover, to reduce the fuzzy system design effort and computational complexity, a novel simultaneous tuning approach is proposed for determining the membership functions and fuzzy rule base. The newly suggested design approach provides a flexible controller with simple structure and straightforward algorithm. To evaluate the effectiveness of the proposed method, the proposed FOFPID controller is applied to solve the Load Frequency Control (LFC) problem in a representative power system with considerations governor saturation and the results are compared to the one obtained by a FOFPID controller with fixed fuzzy part and a fractional order PID (FOPID) controller. Simulation results indicate the superiority of proposed method.

关键词： Fractional order fuzzy PID controller Load frequency control particle swarm optimization algorithm

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Research on Path Planning of Automatic Handling Robot 2

Research on Path Planning of Automatic Handling Robot

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2nd International Conference on Information Technology and Intelligent Transportation Systems (ITITS)

作者： Hou, Yuanbin He, Liu Song, Chunfeng Zhang, Yanyan Li, Dong Xian Univ Sci & Technol Sch Elect & Control Engn Yanta Rd 58 Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781614997856;9781614997849

Aiming at the problem that path planning for automatic handling robot in an environment with obstacles, the working environment model of the handling robot is analyzed. And then a path optimization algorithm based on fusing ant colony and particle swarm optimization algorithm is proposed. First of all, this algorithm uses the global search ability of particle swarm to go on a rough search and quickly plans the starting point to the end of the initial path. Then, the pheromone distribution is performed on the initial path. Finally, the ant colony algorithm is used to search the path carefully to get the optimal path. Experimental verification shows that compared with a single ant colony or particle swarm optimization algorithm, fused algorithm about ant colony and particle swarm optimization has a significant improvement in the number of iterations and path planning.

关键词： Handling robot path planning particle swarm optimization algorithm ant colony algorithm

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