Self-Organizing Neuro-Fuzzy Inference System

被引：0

作者：

Allende-Cid, Hector ^{[1
]}

Veloz, Alejandro ^{[1
]}

Salas, Rodrigo ^{[2
]}

Chabert, Steren ^{[2
]}

Allende, Hector ^{[1
]}

机构：

[1] Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso, Chile

[2] Univ Valparaiso, Dept Ingenier Biomed, Valparaiso, Chile

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS | 2008年 / 5197卷

关键词：

ANFIS; Flexible Architecture; Nonlinear modeling;

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：

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a self-organizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

引用

页码：429 / +

页数：2

共 50 条

[41] Adaptive Neuro-Fuzzy Inference System for drought forecasting
Bacanli, Ulker Guner
Firat, Mahmut
Dikbas, Fatih
[J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2009, 23 (08) : 1143 - 1154
[42] Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with system uncertainties and unknown dead-zone nonlinearity
Liu, Siyuan
Liu, Yancheng
Wang, Ning
[J]. NONLINEAR DYNAMICS, 2017, 89 (02) : 1397 - 1414
[43] Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with system uncertainties and unknown dead-zone nonlinearity
Siyuan Liu
Yancheng Liu
Ning Wang
[J]. Nonlinear Dynamics, 2017, 89 : 1397 - 1414
[44] ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR END MILLING
Markopoulos, Angelos P.
Georgiopoulos, Sotirios
Kinigalakis, Myron
Manolakos, Dimitrios E.
[J]. JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2016, 11 (09) : 1234 - 1248
[45] Adaptive neuro-fuzzy inference system for modelling and control
Amaral, TGB
Crisóstomo, MM
Pires, VF
[J]. 2002 FIRST INTERNATIONAL IEEE SYMPOSIUM INTELLIGENT SYSTEMS, VOL 1, PROCEEDINGS, 2002, : 67 - 72
[46] A New Neuro-Fuzzy Inference System for Insurance Forecasting
Le Hoang Son
Mai Ngoc Khuong
Tran Manh Tuan
[J]. ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY, 2017, 538 : 63 - 72
[47] Adaptive Neuro-Fuzzy Inference System for Financial Evaluation
Orhei, Dragomir
[J]. VISION 2020: SUSTAINABLE GROWTH, ECONOMIC DEVELOPMENT, AND GLOBAL COMPETITIVENESS, VOLS 1-5, 2014, : 241 - 245
[48] Adaptive Neuro-Fuzzy Inference System for drought forecasting
Ulker Guner Bacanli
Mahmut Firat
Fatih Dikbas
[J]. Stochastic Environmental Research and Risk Assessment, 2009, 23 : 1143 - 1154
[49] Adaptive Neuro-Fuzzy Inference System for Classification of Texts
Kamil, Aida-zade
Rustamov, Samir
Clements, Mark A.
Mustafayev, Elshan
[J]. RECENT DEVELOPMENTS AND THE NEW DIRECTION IN SOFT-COMPUTING FOUNDATIONS AND APPLICATIONS, 2018, 361 : 63 - 70
[50] Edge Detection by Adaptive Neuro-Fuzzy Inference System
Zhang, Lei
Xiao, Mei
Ma, Jian
Song, Hongxun
[J]. PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9, 2009, : 1774 - 1777

← 1 2 3 4 5 →