Radar target classification based on radial basis function and modified radial basis function networks

Guosui Liu; Yunhong Wang; Chunling Yang; Dequan Zhou

Radar target classification based on radial basis function and modified radial basis function networks

Guosui Liu^*
, Yunhong Wang
, Chunling Yang
, Dequan Zhou

^*Corresponding author for this work

Nanjing University of Science and Technology

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

This paper discusses the radial basis function (RBF) neural networks used in the radar target classification. To enhance the classification rate, the structure of the modified radial basis function (MRBF) neural network is proposed. Two kinds of MRBF networks which are called the MRBF1 network and the MRBF2 network are discussed in this paper. From the theory as well as computer simulations, we find that the performance of the MRBF network is superior to the RBF network and the MRBF2 network gets higher classification rate than the MRBF1 network.

Original language	English
Pages	208-211
Number of pages	4
State	Published - 1996
Externally published	Yes
Event	Proceedings of the 1996 CIE International Conference of Radar Proceedings, ICR'96 - Beijing, China Duration: 8 Oct 1996 → 10 Oct 1996

Conference

Conference	Proceedings of the 1996 CIE International Conference of Radar Proceedings, ICR'96
City	Beijing, China
Period	8/10/96 → 10/10/96

Cite this

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title = "Radar target classification based on radial basis function and modified radial basis function networks",

abstract = "This paper discusses the radial basis function (RBF) neural networks used in the radar target classification. To enhance the classification rate, the structure of the modified radial basis function (MRBF) neural network is proposed. Two kinds of MRBF networks which are called the MRBF1 network and the MRBF2 network are discussed in this paper. From the theory as well as computer simulations, we find that the performance of the MRBF network is superior to the RBF network and the MRBF2 network gets higher classification rate than the MRBF1 network.",

author = "Guosui Liu and Yunhong Wang and Chunling Yang and Dequan Zhou",

year = "1996",

language = "英语",

pages = "208--211",

note = "Proceedings of the 1996 CIE International Conference of Radar Proceedings, ICR'96 ; Conference date: 08-10-1996 Through 10-10-1996",

}

TY - CONF

T1 - Radar target classification based on radial basis function and modified radial basis function networks

AU - Liu, Guosui

AU - Wang, Yunhong

AU - Yang, Chunling

AU - Zhou, Dequan

PY - 1996

Y1 - 1996

N2 - This paper discusses the radial basis function (RBF) neural networks used in the radar target classification. To enhance the classification rate, the structure of the modified radial basis function (MRBF) neural network is proposed. Two kinds of MRBF networks which are called the MRBF1 network and the MRBF2 network are discussed in this paper. From the theory as well as computer simulations, we find that the performance of the MRBF network is superior to the RBF network and the MRBF2 network gets higher classification rate than the MRBF1 network.

AB - This paper discusses the radial basis function (RBF) neural networks used in the radar target classification. To enhance the classification rate, the structure of the modified radial basis function (MRBF) neural network is proposed. Two kinds of MRBF networks which are called the MRBF1 network and the MRBF2 network are discussed in this paper. From the theory as well as computer simulations, we find that the performance of the MRBF network is superior to the RBF network and the MRBF2 network gets higher classification rate than the MRBF1 network.

UR - https://www.scopus.com/pages/publications/0030395091

M3 - 论文

AN - SCOPUS:0030395091

SP - 208

EP - 211

T2 - Proceedings of the 1996 CIE International Conference of Radar Proceedings, ICR'96

Y2 - 8 October 1996 through 10 October 1996

ER -

Radar target classification based on radial basis function and modified radial basis function networks

Abstract

Conference

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