Reliability Estimation of Three Parameters Weibull Distribution based on Particle Swarm Optimization

The three-parameter Weibull distribution is a continuous distribution widely used in the study of reliability and life data. The estimation of the distribution parameters is an important problem that has received a lot of attention by researchers because of theirs effects in several measurements. In this research, we propose a particle swarm optimization (PSO) to estimate the three-parameter Weibull distribution and then to estimate the reliability and hazard functions. The real data results indicate that our proposed estimation method is significantly consistent in estimation compared to the maximum likelihood method. In terms of log likelihood and mean time to failure (MTTF).

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Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

Open Geosciences ◽

10.1515/geo-2019-0044 ◽

2019 ◽

Vol 11 (1) ◽

pp. 542-548

Author(s):

Wenlong Tang ◽

Hao Cha ◽

Min Wei ◽

Bin Tian ◽

Xichuang Ren

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Communication System ◽

Particle Swarm Optimization Algorithm ◽

Estimation Method ◽

Particle Swarm ◽

Automatic Identification ◽

Identification System ◽

Signal Power ◽

Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

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Method of Optimization for Target Localization Model Parameters Based on LSSVR

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.934 ◽

2011 ◽

Vol 268-270 ◽

pp. 934-939

Author(s):

Xue Wen He ◽

Gui Xiong Liu ◽

Hai Bing Zhu ◽

Xiao Ping Zhang

Keyword(s):

Particle Swarm Optimization ◽

Fitness Function ◽

Particle Swarm ◽

Parameters Optimization ◽

Target Localization ◽

Likelihood Method ◽

Support Vector ◽

Swarm Optimization ◽

Localization Accuracy ◽

Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

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Grain Temperature-Dependent Thermal Properties Estimation Using FI-QPSO Algorithm

Journal of Heat Transfer ◽

10.1115/1.4051701 ◽

2021 ◽

Author(s):

Hongmei Xu ◽

Juan Liu ◽

Kun Wang ◽

Songtao Kong ◽

Yong Shi

Keyword(s):

Particle Swarm Optimization ◽

Thermal Properties ◽

Measurement Errors ◽

Fuzzy Inference ◽

Quantum Particle ◽

Estimation Method ◽

Particle Swarm ◽

Temperature Dependent ◽

Swarm Optimization ◽

Quantum Particle Swarm Optimization

Abstract A hybrid fuzzy inference-quantum particle swarm optimization (FI-QPSO) algorithm is developed to estimate the temperature-dependent thermal properties of grain. The fuzzy inference scheme is established to determine the contraction-expansion coefficient according to the aggregation degree of particles. The heat transfer process in the grain bulk is solved using the finite element method (FEM), and the estimation task is formulated as an inverse problem. Numerical experiments are performed to study the effects of the surface heat flux, number of measurement points, measurement errors and the individual space on the estimation results. Comparison with the quantum particle swarm optimization (QPSO) algorithm and conjugate gradient method (CGM) is also conducted, and it shows the validity of the estimation method established in this paper.

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