Papers by Keyword: Particle Swarm Optimization (PSO)

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Authors: Yi Lei Liu, Dong Gao, Gang Wei Cui
Abstract: Volumetric error has large effect on machine tool accuracy; improving CNC machine tool accuracy through error compensation has received significant attention recently. This paper intends to represent volumetric error measurement based on laser tracker. The volumetric error is modeled by homogenous transformation matrix with each coordinate corresponding to each motion axis. Based on parts of spatial points volumetric error, the geometric errors which affect volumetric positioning error are verified through particle swarm optimization with the L2 parameters as the target function. The chebyshev orthogonal polynomials are applied to approximate geometric errors.
Authors: Hui Min Zhuang, Jian Xiao
Abstract: The correlation between operation parameters including coup de fouet and SOH was analyzed to choose the input parameters of the battery SOH estimation model, and then the battery SOH estimation model was made based on least square support vector machine (LSSVM). For more prediction accuracy and efficiency, the advanced particle swarm optimization (WCPSO) is used to optimize the parameters of the LS-SVM regression model. The battery SOH was estimated only with measured data (plateau voltage, discharge rate and temperature) of short time discharge, so it is efficient. The verification result shows that the WCPSO-LSSVM model can be used to predict the battery SOH, and the precision is above 93%.
Authors: Wen Yuan Xu, Bing Xiang Liu, Xing Xu, Na Hu, Hao Hu, Ying Zhu
Abstract: Water quality parameter is the basic data of the river water quality mathematical model for forecasting river water quality status. In this paper, the parameter estimation problem of the analytical model, which is used to describe one-dimensional tracing test data of river streams with tracers instantaneously injected, is converted to the function optimization problem. And particle swarm optimization algorithm is applied to solve this problem. The experimental results show that the particle swarm optimization algorithm can estimate the water quality model parameter values regardless of whether the randomly sampling data has noise.
Authors: Gui Shui Yu, Ke Li
Abstract: A watershed segmentation algorithm based on fuzzy C-means clustering () was proposed in this paper , which can solve the problem of over-segmentation in sonar image processing. Firstly, the original image was transformed to be gray image and then segmented by watershed algorithm. Secondly, the improved particle swarm optimization () was used to find the accurate original clustering centers of . Finally, with the accurate centers and the improved target function, the small regions of the initial segmented image was clustered by . The iterating number was controlled to increase segmenting speed. Additionally, the high sonar image segmentation efficiency is testified in the experiment and the problem of over-segmentation is restricted.
Authors: Jia Liang Lv, Ying Long Wang, Huan Qing Cui, Nuo Wei
Abstract: Localization is one of the key technologies of wireless sensor networks, and the problem of localization is always formulated as an optimization problem. Particle swarm optimization (PSO) is easy to implement and requires moderate computing resources, which is feasible for localization of sensor networks. To improve the efficiency and precision of PSO-based localization methods, this paper proposes a novel three-dimensional PSO method based on weight selection (WSPSO). Simulation results show that the proposed method outperforms standard PSO and existing localization algorithms.
Authors: Xu Sheng Gan, Hao Lin Cui, Ya Rong Wu
Abstract: In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.
Authors: Jian Xia Wang, Xiang Li
Abstract: For the optimization of WSN coverage, this paper proposes a coverage optimization approach: Adaptive Disturbance Chaotic Particle Swarm Optimization, referred as ADCPSO. Based on the effective coverage of the network as the optimization goal, the method first conducts adaptive operation on the particles. Introducing perturbations factor and making particles trapped into local optimum quickly jump out, this method then use the randomness and the ergodicity of chaotic motion, to make local fine search. This effectively avoids particles’ “being premature” and improves the accuracy of the algorithm. The simulation results show that the ADCPSO algorithm can get better coverage.
Authors: Wen Tsai Sung, Chia Cheng Hsu, Jui Ho Chen
Abstract: This paper improve the original particle swarm optimization (PSO) algorithm in the fixed inertia weight, this study analyzed the value of inertia weight factor in (PSO) algorithm, and proposed a non-linear weights with decreasing strategy to implement the Improvement PSO (IPSO) algorithm and estimate the weighted factor in the data fusion of multi-sensors network. This FLAG Programmable System On Chip (SoC) Developing System 1605A (FLAG-the PSoC-1605A) as an experimental platform, using various types of sensors, combined with ZigBee wireless sensor networks, the TCP / IP network and the GPRS / SMS long-range wireless network will sense the measured data analysis and evaluation, to create a more effective monitoring and observing regional environment to achieve a things and things, and automated exchange of information between persons and things, processing an intelligent network. Finally, the IPSO applied the IOT system in monitoring environment is better than other existing PSO method in computing precise and convergence rate for excellent fusion result.
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