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
下载
收藏
页码：3263 / 3268
页数：6
相关论文
共 50 条
  • [1] A SELF-ADAPTIVE QUANTUM RADIAL BASIS FUNCTION NETWORK FOR CLASSIFICATION APPLICATIONS
    Lin, Cheng-Jian
    Peng, Chun-Cheng
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2011, 7 (12): : 6621 - 6634
  • [2] Adaptive image classification with radial basis function network
    Pun, CM
    PROCEEDINGS OF THE SEVENTH IASTED INTERNATIONAL CONFERENCE ON COMPUTER GRAPHICS AND IMAGING, 2004, : 389 - 394
  • [3] Self-adaptive radial basis function neural network for short-term electricity price forecasting
    Meng, K.
    Dong, Z. Y.
    Wong, K. P.
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2009, 3 (04) : 325 - 335
  • [4] Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network
    Nie, Linzhen
    Guan, Jiayi
    Lu, Chihua
    Zheng, Hao
    Yin, Zhishuai
    IET INTELLIGENT TRANSPORT SYSTEMS, 2018, 12 (06) : 485 - 494
  • [5] A novel self-adaptive extreme learning machine based on affinity propagation for radial basis function neural network
    Shifei Ding
    Gang Ma
    Zhongzhi Shi
    Neural Computing and Applications, 2014, 24 : 1487 - 1495
  • [6] A novel self-adaptive extreme learning machine based on affinity propagation for radial basis function neural network
    Ding, Shifei
    Ma, Gang
    Shi, Zhongzhi
    NEURAL COMPUTING & APPLICATIONS, 2014, 24 (7-8): : 1487 - 1495
  • [7] Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database
    Dash, Ch. Sanjeev Kumar
    Saran, Amitav
    Sahoo, Pulak
    Dehuri, Satchidananda
    Cho, Sung-Bae
    PATTERN RECOGNITION LETTERS, 2016, 80 : 76 - 83
  • [8] QRS classification using Adaptive Hermite Decomposition and Radial Basis Function Network
    Zeng, HJ
    Wu, H
    Lin, JR
    PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 20, PTS 1-6: BIOMEDICAL ENGINEERING TOWARDS THE YEAR 2000 AND BEYOND, 1998, 20 : 147 - 150
  • [9] PROTEIN SUPERFAMILY CLASSIFICATION USING ADAPTIVE EVOLUTIONARY RADIAL BASIS FUNCTION NETWORK
    Vipsita, Swati
    Rath, Santanu
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2012, 11 (04)
  • [10] Adaptive equalizer using self-generating radial basis function network
    Kuwata, K
    Watanabe, M
    Katayama, R
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 1997, 5 (01) : 23 - 31
12345