Neural Network with Lower and Upper Type-2 Fuzzy Weights using the Backpropagation Learning Method

被引：0
|
作者：
Gaxiola, Fernando [1 ]
Melin, Patricia [1 ]
Valdez, Fevrier [1 ]
机构：
[1] Tijuana Inst Technol, Tijuana, Mexico
来源：
PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS) | 2013年
关键词：
Neural Networks; Type-2 Fuzzy Weights; Backpropagation Algorithm; Type-2 fuzzy system; SYSTEMS; ALGORITHM; LOGIC; OPTIMIZATION; DESIGN; SPEED;
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 the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.
引用
收藏
页码：637 / 642
页数：6
相关论文
共 50 条
  • [31] Dynamic Fuzzy Learning Rate in a Self-Evolving Interval Type-2 TSK Fuzzy Neural Network
    Tolue, Shirin Fartah
    Akbarzadeh-T., Mohammad-R.
    2013 13TH IRANIAN CONFERENCE ON FUZZY SYSTEMS (IFSC), 2013,
  • [32] A New Type of Fuzzy Membership Function Designed for Interval Type-2 Fuzzy Neural Network
    Wang J.
    Wang, Jiajun (wangjiajun@hdu.edu.cn), 2017, Science Press (43): : 1425 - 1433
  • [33] Type-2 Fuzzy Clustering and a Type-2 Fuzzy Inference Neural Network for the Prediction of Short-Term Interest Rates
    Enke, David
    Mehdiyev, Nijat
    COMPLEX ADAPTIVE SYSTEMS: EMERGING TECHNOLOGIES FOR EVOLVING SYSTEMS: SOCIO-TECHNICAL, CYBER AND BIG DATA, 2013, 20 : 115 - 120
  • [34] Type-2 Fuzzy Hypergraphs Using Type-2 Fuzzy Sets
    Park, Seihwan
    Lee-Kwang, Hyung
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2000, 4 (05) : 362 - 367
  • [35] Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction
    Gaxiola, Fernando
    Melin, Patricia
    Valdez, Fevrier
    Castillo, Oscar
    INFORMATION SCIENCES, 2014, 260 : 1 - 14
  • [36] A type-2 fuzzy wavelet neural network for system identification and control
    Abiyev, Rahib H.
    Kaynak, Okyay
    Kayacan, Erdal
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2013, 350 (07): : 1658 - 1685
  • [37] Cooperative strategy for constructing interval type-2 fuzzy neural network
    Han, Hong-Gui
    Li, Jia-Ming
    Wu, Xiao-Long
    Qiao, Jun-Fei
    NEUROCOMPUTING, 2019, 365 : 249 - 260
  • [38] Design of interval type-2 fuzzy neural network system and application
    Wang, Tao
    Han, Chunyu
    Jin, Xuelian
    ICIC Express Letters, Part B: Applications, 2015, 6 (04): : 1041 - 1047
  • [39] A Type-2 Fuzzy Wavelet Neural Network for Time Series Prediction
    Abiyev, Rahib H.
    TRENDS IN APPLIED INTELLIGENT SYSTEMS, PT III, PROCEEDINGS, 2010, 6098 : 518 - 527
  • [40] Type-2 Fuzzy Neural Network Controller for a class of Nonlinear Systems
    Farahani, Hossein Moradi
    Askari, Javad
    Zekri, Maryam
    Kamali, Marzieh
    2013 21ST IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2013,
12345