scholarly journals A Hybrid Rough Set--Particle Swarm Algorithm for Image Pixel Classification

2006 ◽  
Author(s):  
Swagatam Das ◽  
Ajith Abraham ◽  
Subir Sarkar
Keyword(s):  
Rough Set ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Pixel Classification ◽  
Swarm Algorithm

Analysis of Attribute Reduction of Rough Set and CNC Machine Fault Diagnosis Based on Particle Swarm

2012 ◽  
Vol 214 ◽  
pp. 835-839
Author(s):  
Zhuang Wu
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Attribute Reduction ◽  
Particle Swarm ◽  
Fault Identification ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Cnc Machine ◽  
Related Data ◽  
Swarm Algorithm

After reasoning and calculation, fault diagnosis can automatically identify the causes of malfunction based on the fault symptoms, which is the core task of fault diagnosis. This paper applies the particle swarm algorithm and rough set to the fault diagnosis, and proposes fault diagnosis knowledge acquisition, rules optimization and fault identification based on rough set attribute reduction of particle swarm. Firstly, this paper introduces the rough set attribute reduction. Secondly, the particle swarm algorithm is applied to the rough set attribute reduction algorithm. Finally, the correctness and superiority of this algorithm are proved from the reduction experimental results of related data sets.

scholarly journals A Novel Back Propagation Neural Network Optimized by Rough Set and Particle Swarm Algorithm for Remanufacturing Service Provider Classification and Selection

2021 ◽  
Vol 2083 (4) ◽  
pp. 042058
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Xiang Liu ◽  
Zelin Zhang
Keyword(s):  
Neural Network ◽  
Rough Set ◽  
Service Provider ◽  
Service Providers ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Particle Swarm Algorithm ◽  
Initial Weight ◽  
Swarm Algorithm

Abstract Aiming at the problem that the high classification feature dimensionality of the back propagation neural network (BPNN) leads to slow convergence speed and the initial weight and threshold sensitivity of the BPNN lead to the problem of easy convergence to the local optimum. A novel BPNN optimized by rough set and particle swarm algorithm (RS-PSO-BPNN) for remanufacturing service provider classification and selection is proposed. First, the attribute reduction method of rough set theory is used to preprocess the classification features of remanufacturing service providers, redundant attributes are deleted from the decision table, and the input feature dimension is reduced; then the PSO algorithm is used to optimize the network Initial weight and threshold. Finally, the proposed method is used for the selection and optimization of remanufacturing service providers. The results show that the proposed RS-PSO-BPNN has higher classification accuracy and efficiency for the problem, which provides scientific decision supports for remanufacturing service provider selection.

Dynamic simulation of wind field in three-dimensional display space considering particle swarm algorithm fuzzy logic

2021 ◽  
pp. 1-6
Author(s):  
Honglei Xu ◽  
Linhuan Wang
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Simulation ◽  
Wind Field ◽  
Three Dimensional ◽  
Measurement Data ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Three Dimensional Display ◽  
Swarm Algorithm ◽  
Synchronous Measurement

In order to improve the accuracy of dynamic detection of wind field in the three-dimensional display space, system software is carried out on the actual scene and corresponding airborne radar observation information data, and the particle swarm algorithm fuzzy logic algorithm is introduced into the wind field dynamic simulation process in three-dimensional display space, to analyze the error of the filtering result in detail, to process the hurricane Lily Doppler radar measurement data with the optimal adaptive filtering according to the error data. The three-dimensional wind field synchronous measurement data obtained by filtering was compared with three-dimensional wind field synchronous measurement data of the GPS dropsonde in this experiment, the sea surface wind field measurement data of the multi-band microwave radiometer, and the wind field data at aircraft altitude.

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