A neuro-fuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning

2003 ◽  
Vol 11 (3) ◽  
pp. 341-353 ◽  
Author(s):  
Shie-Jue Lee ◽  
Chen-Sen Ouyang
Keyword(s):  
Fuzzy System ◽  
System Modeling ◽  
Rule Generation ◽  
Neuro Fuzzy

Neuro-Fuzzy System Modeling

2010 ◽  
pp. 147-175
Author(s):  
Chen-Sen Ouyang
Keyword(s):  
Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
System Modeling ◽  
Fuzzy Modeling ◽  
Structure Identification ◽  
Learning Capability ◽  
Computing Paradigm ◽  
Emerging Trends ◽  
Neuro Fuzzy

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

scholarly journals Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System

2005 ◽  
Vol 2 (1) ◽  
pp. 12
Author(s):  
E. A. Al-Gallaf
Keyword(s):  
Dynamic System ◽  
Fuzzy System ◽  
System Modeling ◽  
Nonlinear Dynamic System ◽  
Fuzzy Modeling ◽  
Antenna System ◽  
Approximation Techniques ◽  
Intelligent Modeling ◽  
Neuro Fuzzy ◽  
Takagi Sugeno

This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules. 

scholarly journals A Neuro-Fuzzy System Modeling using Gaussian Mixture Model and Clustering Method

2002 ◽  
Vol 12 (6) ◽  
pp. 571-576
Author(s):  
Sung-Suk Kim ◽  
Keun-Chang Kwak ◽  
Jeong-Woong Ryu ◽  
Myung-Geun Chun
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fuzzy System ◽  
System Modeling ◽  
Gaussian Mixture ◽  
Clustering Method ◽  
Neuro Fuzzy

Neuro-fuzzy system modeling based on automatic fuzzy clustering

2005 ◽  
Vol 3 (2) ◽  
pp. 121-130 ◽  
Author(s):  
Yuangang Tang ◽  
Fuchun Sun ◽  
Zengqi Sun
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy System ◽  
System Modeling ◽  
Neuro Fuzzy

scholarly journals Modeling of neuro-fuzzy system as a support in decision-making processes

2021 ◽  
Vol 2 (1) ◽  
pp. 222-234
Author(s):  
Darko Bozanic ◽  
◽  
Duško Tešić ◽  
Dragan Marinkovic ◽  
Aleksandar Milić ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy System ◽  
System Modeling ◽  
Multi Criteria Decision Making ◽  
Neuro Fuzzy ◽  
Decision Making Processes ◽  
Model Training ◽  
Input Variables ◽  
Selection Of ◽  
Decision Making Support

In the paper is presented Neuro-Fuzzy System as a decision-making support in the selection of construction machines (the example of selecting a loader is provided). Construction characteristics of a loader make the basis for selection, but also other elements of importance. The data for Neuro-Fuzzy System modeling are prepared using the Multi-Criteria Decision Making (MCDM) methods: Logarithm Methodology of Additive Weights (LMAW), VIKOR, TOPSIS, MOORA and SAW. The paper also presents the method of aggregation of weights of rules premises (AWRP), which defines the key rules of Neuro-Fuzzy System. Finally, the training of the model is tested. The data for the selection of input variables and for model training are obtained by engaging experts.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

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