Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition
In order to overcome the long training time caused by searching optimal basic functions based on greedy strategy from a redundant basis function dictionary for the intuitionistic fuzzy kernel matching pursuit (IFKMP), the particle swarm optimization algorithm with powerful ability of global search a...
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2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/587925 |
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doaj-54f814285f7d43d9973b210f10d88d1d2020-11-25T01:08:03ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/587925587925Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target RecognitionXiaodong Yu0Yingjie Lei1Shaohua Yue2Feixiang Meng3Air Defense and Antimissile Institute, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile Institute, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile Institute, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile Institute, Air Force Engineering University, Xi’an 710051, ChinaIn order to overcome the long training time caused by searching optimal basic functions based on greedy strategy from a redundant basis function dictionary for the intuitionistic fuzzy kernel matching pursuit (IFKMP), the particle swarm optimization algorithm with powerful ability of global search and quick convergence rate is applied to speed up searching optimal basic function data in function dictionary. The approach of intuitionistic fuzzy kernel matching pursuit based on particle swarm optimization algorithm, namely, PS-IFKMP, is proposed. This algorithm is applied to the aerospace target recognition, which requires real-time ability. Simulation results show that, compared with the conventional approaches, the proposed algorithm can decrease training time and improve calculation efficiency obviously with almost unchanged classification accuracy, while the model has better sparsity and generalization. It is also demonstrated that this approach is suitable for the application requiring both accuracy and efficiency.http://dx.doi.org/10.1155/2015/587925 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaodong Yu Yingjie Lei Shaohua Yue Feixiang Meng |
spellingShingle |
Xiaodong Yu Yingjie Lei Shaohua Yue Feixiang Meng Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition Mathematical Problems in Engineering |
author_facet |
Xiaodong Yu Yingjie Lei Shaohua Yue Feixiang Meng |
author_sort |
Xiaodong Yu |
title |
Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition |
title_short |
Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition |
title_full |
Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition |
title_fullStr |
Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition |
title_full_unstemmed |
Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition |
title_sort |
intuitionistic fuzzy kernel matching pursuit based on particle swarm optimization for target recognition |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
In order to overcome the long training time caused by searching optimal basic functions based on greedy strategy from a redundant basis function dictionary for the intuitionistic fuzzy kernel matching pursuit (IFKMP), the particle swarm optimization algorithm with powerful ability of global search and quick convergence rate is applied to speed up searching optimal basic function data in function dictionary. The approach of intuitionistic fuzzy kernel matching pursuit based on particle swarm optimization algorithm, namely, PS-IFKMP, is proposed. This algorithm is applied to the aerospace target recognition, which requires real-time ability. Simulation results show that, compared with the conventional approaches, the proposed algorithm can decrease training time and improve calculation efficiency obviously with almost unchanged classification accuracy, while the model has better sparsity and generalization. It is also demonstrated that this approach is suitable for the application requiring both accuracy and efficiency. |
url |
http://dx.doi.org/10.1155/2015/587925 |
work_keys_str_mv |
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