A self-adaptive quantum radial basis function network for classification applications

被引：0

作者：

Lin, CJ ^{[1
]}

Chen, CH ^{[1
]}

Lee, CY ^{[1
]}

机构：

[1] Chaoyang Univ Technol, Dept Comp Sci & Informat Engn, Taichung 413, Taiwan

来源：

2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2004年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a self-adaptive quantum radial basis function network (QRBFN) is proposed for classification applications. The QRBFN model is a three-layer structure. The hidden layer of the QRBFN model contains quantum function neurons, which are multilevel activation functions. Each quantum function neuron is composed of the sum of sigmoid functions shifted by quantum intervals. A self-adaptive learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is proposed. The proposed the SCA method is a fast, one-pass algorithm for a dynamic estimation of the number of clusters in an input data space. The backpropagation algorithm is used to tune the adjustable parameters. Simulation results were conducted to show the performance and applicability of the proposed model.

引用

页码：3263 / 3268

页数：6

共 50 条

[31] A Self-adaptive Network Traffic Classification System with Unknown Flow Detection [J].

Ran, Jing ;

Kong, Xiaochen ;

Lin, Gan ;

Yuan, Dongming ;

Hu, Hefei .

PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2017, :1215-1220

[32] A Multiscale Self-Adaptive Attention Network for Remote Sensing Scene Classification [J].

Li, Lingling ;

Liang, Pujiang ;

Ma, Jingjing ;

Jiao, Licheng ;

Guo, Xiaohui ;

Liu, Fang ;

Sun, Chen .

REMOTE SENSING, 2020, 12 (14)

[33] A strategy for an efficient training of radial basis function networks for classification applications [J].

Buchtala, O ;

Neumann, P ;

Sick, B .

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, :1025-1030

[34] EMG Signal Classification Using Radial Basis Function Neural Network [J].

AlKhazzar, Ahmed Mohammed ;

Raheema, Mithaq Nama .

2018 THIRD SCIENTIFIC CONFERENCE OF ELECTRICAL ENGINEERING (SCEE), 2018, :180-185

[35] A Deep Neural Network Combined with Radial Basis Function for Abnormality Classification [J].

Noushin Jafarpisheh ;

Effat J. Zaferani ;

Mohammad Teshnehlab ;

Hadis Karimipour ;

Reza M. Parizi ;

Gautam Srivastava .

Mobile Networks and Applications, 2021, 26 :2318-2328

[36] Radial basis function (RBF) network adaptive power system stabilizer [J].

Segal, R ;

Kothari, ML ;

Madnani, S .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2000, 15 (02) :722-727

[37] Adaptive domain beamforming for OFDM using radial basis function network [J].

Wu, Yucheng ;

Ren, Guangxi ;

Zhao, Wengang .

2007 IEEE INTERNATIONAL CONFERENCE ON INTEGRATION TECHNOLOGY, PROCEEDINGS, 2007, :63-+

[38] Reducing the complexity of an adaptive radial basis function network with a histogram algorithm [J].

Pey Yun Goh ;

Shing Chiang Tan ;

Wooi Ping Cheah ;

Chee Peng Lim .

Neural Computing and Applications, 2017, 28 :365-378

[39] Classification of gear faults using cumulants and the radial basis function network [J].

Lai, WX ;

Tse, PW ;

Zhang, GC ;

Shi, TL .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2004, 18 (02) :381-389

[40] Radial basis function based probabilistic neural network in arrhythmia classification [J].

Chen, Chongyang ;

Cai, Ping ;

Shi, Wenkang ;

Guo, nengwu .

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2000, 34 (11) :1475-1477

← 1 2 3 4 5 →