Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 708-715
Author(s):  
Xiaobin Xu ◽  
Minzhou Luo ◽  
Zhiying Tan ◽  
Min Zhang ◽  
Hao Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Adaptive Kalman Filter ◽  
Swarm Optimization ◽  
Content Type ◽  
Kalman Filter Algorithm

Purpose This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm. Design/methodology/approach A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function. Findings The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement. Originality/value The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

scholarly journals Research on Timetabling Problems Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 736-741
Author(s):  
Xin Min Ma ◽  
Lin Li Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Educational System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Course Timetabling ◽  
Fitness Value ◽  
Course Timetabling Problem

A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

scholarly journals An improved dynamic cooperative random drift particle swarm optimization algorithm based on search history decision

2020 ◽  
Vol 14 ◽  
pp. 174830262097353
Author(s):  
Ji Zhao ◽  
Yi Fu ◽  
Juan Mei
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Function Approximation ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Random Drift ◽  
Swarm Optimization ◽  
Search History ◽  
Drift Particle

A novel dynamic cooperative random drift particle swarm optimization algorithm based on entire search history decision (CRDPSO) is reported. At each iteration, the positions and the fitness values of the evaluated solutions in the algorithm are stored by a binary space partitioning tree structure archive, which leads to a fast fitness function approximation. The mutation is adaptive and parameter-less because of the fitness function approximation enhancing the mutation strategy. The dynamic cooperation between the particles by using the context vector increases the population diversity helps to improve the search ability of the swarm and cooperatively searches for the global optimum. The performance of CRDPSO is tested on standard benchmark problems including multimodal and unimodal functions. The empirical results show that CRDPSO outperforms other well-known stochastic optimization methods.

Application of PSO algorithm to choose the best scenario for energy demand forecast of residential and commercial sectors in Iran

2016 ◽  
Vol 10 (4) ◽  
pp. 576-593
Author(s):  
Hesam Nazari ◽  
Aliyeh Kazemi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Energy Demand ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Suitable Model ◽  
Demand Forecast ◽  
Swarm Optimization ◽  
Content Type ◽  
Residential And Commercial Sectors

Purpose This paper aims to select the best scenario for energy demand forecast of residential and commercial sectors in Iran by using particle swarm optimization algorithm. Design/methodology/approach In this study, using variables affecting energy demand of residential and commercial sectors in Iran, the future status of energy demand in these sectors is predicted. Using the particle swarm optimization algorithm, both linear and exponential forms of energy demand equations were studied under 72 different scenarios with various variables. The data from 1968 to 2011 were applied for model development and the appropriate scenario choice. Findings An exponential model with inputs including total value added minus that of the oil sector, value of made buildings, total number of households and consumer energy price index is the most suitable model. Finally, energy demand of residential and commercial sectors is estimated up to the year 2032. Results show that the energy demand of the sectors will achieve a level of about 1,718 million barrels of oil equivalent per year by 2032. Originality/value To the best of our knowledge in this study a suitable model is selected for energy demand forecast of residential and commercial sectors by evaluation of various models with different variables as inputs.

Study on Dynamic Balance in Main Driving Mechanism of Cold Rolling Mill Based on the Particle Swarm Optimization Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 633-638 ◽  
Author(s):  
Wen Xian Tang ◽  
Jun Jie Sun ◽  
Bin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Dynamic Balance ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Inertia Force ◽  
Driving Mechanism ◽  
Swarm Optimization

A method for comprehensive dynamic balance of mechanism based on the particle swarm optimization is presented. This paper adopted nonlinear multi-objective programming method to carry out a study on three dynamic property indexes including inertia force, reaction of kinematic pair and input torque. Optimum solution for the parameters estimation problem based on the particle swarm optimization algorithm is obtained by constructing a fitness function of the mathematical optimization model, which consists of those property indexes. The simulation results indicate that the proposed method could eliminate the reluctant evaluations and interactions remarkably, thus improves the application's performance.

Application of Synergistic Optimization Method by Maximin Fitness Function Strategy Based Multi-Objective Particle Swarm Optimization Algorithm in Metal Bellows Structural Design

2013 ◽  
Vol 722 ◽  
pp. 550-556
Author(s):  
Xiao Mo Yu ◽  
Ai Ling Qin ◽  
Jia Hai Xue ◽  
Jun Ke Ye ◽  
Wen Jing Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Structure Design ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Metal Bellows ◽  
Function Strategy

In this paper, the forming process is applied to the structure design of the metal bellows for the synergistic optimization.With bellows minimum overall stiffness and minimum weight for the optimization objectives to establish multi-objective optimization design model, using the Maximin fitness function strategy based multi-objective particle swarm optimization algorithm and introduce the multiple subgroup cooperative search strategy by master-slave clustering to get the optimized solution at the same time. The algorithm is applied to the synergistic optimization of the metal bellows structure design. The results show that the convergent speed of the algorithm is fast and can effectively approximate the actual bellows structure design, and provide users with more practical and intuitive effectively design scheme.

scholarly journals Fitness Function Based Particle Swarm Optimization Algorithm for Mobile Adhoc Networks

2018 ◽  
Vol 7 (3.1) ◽  
pp. 31
Author(s):  
Rohan Gupta ◽  
Gurpreet Singh ◽  
Amanpreet Kaur ◽  
Aashdeep Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Network Performance ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Delivery Ratio ◽  
Average Delay ◽  
Swarm Optimization ◽  
Mobile Adhoc Network

Mobile adhoc network is a network which carries out discussion between nodes in the absence of infrastructure. The fitness function based Particle Swarm Optimization Algorithm has been projected for improving the network performance. The effect of changing the number of nodes, communication range and transmission range is investigated on various qualities of service metrics namely packet delivery ratio, throughput and average delay. The investigation has been carried out using NS-2 simulator.  

Study of Simulation Test Generation Method Based on Improved Binary Particle Swarm Optimization Algorithm

2013 ◽  
Vol 823 ◽  
pp. 661-664
Author(s):  
Guang Yao Lian ◽  
Peng Cheng Yan ◽  
Jiang Sheng Sun ◽  
Kao Li Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Test Generation ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Binary Particle Swarm Optimization ◽  
Simulation Test ◽  
Swarm Optimization

To solve the backdating problem of traditional test generation methods, it puts forward a new test generation method based on improved binary particle swarm optimization algorithm in the paper. It estates the fitness function of test vector and faults in the circuits, and the optimal solution is the maximal value of the function. The experimentations prove that the method can reduce the compute quantity of test generation.

Band Selection for Hyperspectral Image Classification Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 889-890 ◽  
pp. 1073-1077 ◽  
Author(s):  
Chen Ming Li ◽  
Yan Wang ◽  
Hong Min Gao ◽  
Li Li Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Hyperspectral Image ◽  
Fitness Function ◽  
Particle Swarm ◽  
Hyperspectral Images ◽  
Band Selection ◽  
Swarm Optimization ◽  
Selection Of

Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. An application of particle swarm optimization algorithm based on B distance was proposed to band selection of hyperspectral images. First of all, bands are grouping by the correlation coefficient of the band and adjacent bands. B distance was used as separability criterion between classes and the fitness function comes into being. Finally, the classification results illustrate that the total classification accuracy of the proposed method is higher than the traditional method.

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