Recognition of handwritten characters using modified fuzzy hyperline segment neural network

被引：0
|
作者：
Patil, PM [1 ]
Dhabe, PS [1 ]
Kulkarni, U [1 ]
Sontakke, TR [1 ]
机构：
[1] SGGS Coll Engn & Technol, Elect & Comp Sci & Engn Dept, Nanded 431602, India
来源：
PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2 | 2003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 membership function of fuzzy hyperline segment neural network (FHLSNN) proposed by U.V.Kulkarni and T.R.Sontakke is modified to maintain convexity. The modified membership function is found superior than the function defined by them, which gives relatively lower values to the patterns which are failing close to the hyperline segment (HLS) but far from two end points of HLS. The performance of modified fuzzy hyperline segment neural network (MFHLSNN) is tested with the two splits of FISHER IRIS data and is found superior than FHLSNN. The modified neural network is also found superior than the general fuzzy min-max neural network (GFMM), proposed by Bogdan Gabrys and Andrzej Bargiela, and general fuzzy hypersphere neural network (GFHSNN), proposed by U. V. Kulkarni, D.D.Doye and T. R. Sontakke.
引用
收藏
页码：1418 / 1422
页数：5
相关论文
共 50 条
  • [41] Handwritten Tifinagh Characters Recognition Using Deep Convolutional Neural Networks
    Benaddy, Mohamed
    El Meslouhi, Othmane
    Es-saady, Youssef
    Kardouchi, Mustapha
    SENSING AND IMAGING, 2019, 20 (1):
  • [42] Hybrid Feature Vector for the Recognition of Arabic Handwritten Characters Using Feed-Forward Neural Network
    Lamghari, N.
    Charaf, M. E. H.
    Raghay, S.
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (12) : 7031 - 7039
  • [43] A NEURAL-NETWORK ASSOCIATIVE MEMORY FOR HANDWRITTEN CHARACTER-RECOGNITION USING MULTIPLE CHUA CHARACTERS
    BAIRD, B
    HIRSCH, MW
    EECKMAN, F
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING, 1993, 40 (10): : 667 - 674
  • [44] Hybrid Feature Vector for the Recognition of Arabic Handwritten Characters Using Feed-Forward Neural Network
    N. Lamghari
    M. E. H. Charaf
    S. Raghay
    Arabian Journal for Science and Engineering, 2018, 43 : 7031 - 7039
  • [45] Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network
    Hazrat Ali
    Ahsan Ullah
    Talha Iqbal
    Shahid Khattak
    SN Applied Sciences, 2020, 2
  • [46] Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network
    Ali, Hazrat
    Ullah, Ahsan
    Iqbal, Talha
    Khattak, Shahid
    SN APPLIED SCIENCES, 2020, 2 (02):
  • [47] Interactive Fuzzy Model Based Recognition of Handwritten Characters
    Murthy, O. V. Ramana
    Hanmandlu, M.
    JOURNAL OF PATTERN RECOGNITION RESEARCH, 2011, 6 (02): : 154 - 165
  • [48] Recognition of Tifinaghe Characters Using Neural Network
    El Ayachi, Rachid
    Fakir, Mohamed
    2009 INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS (ICMCS 2009), 2009, : 317 - 321
  • [49] Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition
    Popko, E. A.
    Weinstein, I. A.
    5TH INTERNATIONAL CONFERENCE ON MATHEMATICAL MODELING IN PHYSICAL SCIENCES (IC-MSQUARE 2016), 2016, 738
  • [50] The association of genetic algorithm and fuzzy neural network in handwritten character recognition
    Bac, LH
    Thai, LH
    KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2, 2001, 69 : 1538 - 1544
12345