A neuro-fuzzy approach to optimize hierarchical recurrent fuzzy systems

被引：7
|
作者：
Nürnberger A. [1 ]
Kruse R. [2 ]
机构：
[1] University of California at Berkeley, EECS, Computer Science Division, Berkeley
[2] University of Magdeburg, Faculty of Computer Science
来源：
Fuzzy Optimization and Decision Making | 2002年 / 1卷 / 2期
关键词：
Decision support; Hierarchical fuzzy system; Hybrid system; Neuro-fuzzy; Recurrent architecture;
D O I：
10.1023/A:1015739303105
中图分类号：
学科分类号：
摘要：
To simplify the definition of fuzzy systems or to reduce its complexity hierarchical structures can be used. Thus, more transparent rule bases that are also easier to maintain can be designed. Furthermore, it is sometimes necessary to use time delayed input or to reuse time delayed output from the fuzzy system itself to obtain a rule base that describes the analyzed problem appropriately. This leads to hierarchical recurrent architectures that have increased approximation capabilities since they are able to store information of the past. In this article we present a neuro-fuzzy model that can be used to optimize hierarchical recurrent fuzzy rule bases if training data is available. Furthermore, we present an approach to learn initial rule bases from data using rule templates. © 2002 Kluwer Academic Publishers.
引用
下载
收藏
页码：221 / 248
页数：27
相关论文
共 50 条
  • [41] Recurrent neuro-fuzzy modeling and fuzzy MDPP control for flexible servomechanisms
    Lin, CS
    Yang, TC
    Jou, YC
    Lin, LC
    JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2003, 38 (02) : 213 - 235
  • [42] Constructive approach to neuro-fuzzy networks
    Univ of Rome `La Sapienza', Rome, Italy
    Signal Process, 3 (347-358):
  • [43] A neuro-fuzzy approach in parts clustering
    Pai, PF
    PEACHFUZZ 2000 : 19TH INTERNATIONAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS, 2000, : 138 - 142
  • [44] Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms
    Chorng-Shyan Lin
    Tachung Yang
    Yeong-Chau Jou
    Lih-Chang Lin
    Journal of Intelligent and Robotic Systems, 2003, 38 : 213 - 235
  • [45] An efficient recurrent neuro-fuzzy system for identification and control of dynamic systems
    Wang, JS
    2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2003, : 2833 - 2838
  • [46] A neuro-fuzzy approach to face recognition
    Neagoe, VE
    Iatan, IF
    6TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XIV, PROCEEDINGS: IMAGE, ACOUSTIC, SPEECH AND SIGNAL PROCESSING III, 2002, : 120 - 125
  • [47] A neuro-fuzzy approach in student modeling
    Stathacopoulou, R
    Grigoriadou, M
    Magoulas, GD
    Mitropoulos, D
    USER MODELING 2003, PROCEEDINGS, 2003, 2702 : 337 - 341
  • [48] Design of experiments in neuro-fuzzy systems
    Zanchettin, C
    Minku, FL
    Ludermir, TB
    HIS 2005: 5TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS, PROCEEDINGS, 2005, : 218 - 223
  • [49] Superior neuro-fuzzy classification systems
    Azar, Ahmad Taher
    El-Said, Shaimaa Ahmed
    NEURAL COMPUTING & APPLICATIONS, 2013, 23 : S55 - S72
  • [50] Monitoring approach using recurrent radial basis function neural networks and neuro-fuzzy systems
    Racoceanu, D
    Zerhouni, N
    PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3, 2005, : 678 - 682
12345