LS-optimal fuzzy modelling and its application to pneumatic drives

1997 ◽  
Author(s):  
Thomas Bernd ◽  
Andreas Kroll ◽  
Helmut Schwarz
Keyword(s):  
Fuzzy Modelling

Evaluation of a 3PL company: an approach of fuzzy modelling

2014 ◽  
Vol 6 (2) ◽  
pp. 131 ◽  
Author(s):  
Ankit Bansal ◽  
Pravin Kumar ◽  
Siddhant Issar
Keyword(s):  
Fuzzy Modelling

Fuzzy relation equations theory as a basis of fuzzy modelling: An overview

1991 ◽  
Vol 40 (3) ◽  
pp. 415-429 ◽  
Author(s):  
A. Di Nola ◽  
W. Pedrycz ◽  
S. Sessa ◽  
E. Sanchez
Keyword(s):  
Fuzzy Relation ◽  
Fuzzy Modelling ◽  
Fuzzy Relation Equations

Greenhouse climate hierarchical fuzzy modelling

2005 ◽  
Vol 13 (5) ◽  
pp. 613-628 ◽  
Author(s):  
Paulo Salgado ◽  
J.Boaventura Cunha
Keyword(s):  
Fuzzy Modelling ◽  
Greenhouse Climate

Application of fuzzy modelling to predict the earthquake damage degree of buildings based on field data

2021 ◽  
pp. 1-14
Author(s):  
Mohammad Reza Amiri Shahmirani ◽  
Abbas Akbarpour Nikghalb Rashti ◽  
Mohammad Reza Adib Ramezani ◽  
Emadaldin Mohammadi Golafshani
Keyword(s):  
Structural Damage ◽  
Input Data ◽  
Field Data ◽  
Earthquake Occurrence ◽  
Effective Parameters ◽  
Fuzzy Modelling ◽  
Damage Degree ◽  
Input And Output ◽  
The City ◽  
Slight Damage

Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data.

On interval-data based Type-1 Takagi-Sugeno fuzzy systems for uncertain nonlinear dynamic system identification

2016 ◽  
Vol 64 (6) ◽  
Author(s):  
Salman Zaidi ◽  
Andreas Kroll
Keyword(s):  
Fuzzy System ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic System ◽  
Random Variable ◽  
Interval Data ◽  
Fuzzy Modelling ◽  
Symbolic Data ◽  
Specific Assumption ◽  
Takagi Sugeno

AbstractA novel interval-data based Takagi-Sugeno fuzzy system is proposed to identify uncertain nonlinear dynamic systems by endowing the classical TS fuzzy system with probability theory and symbolic data analysis. Such systems have variability in their outputs, that is they produce varying responses each time when the same stimuli is applied to them under the same condition. Interval data is generated by repeating the identification experiment multiple times and applying the probabilistic techniques to get soft bounds of output. The interval data is then directly used in the TS fuzzy modelling, giving rise to interval antecedent and consequent parameters. This method does not require any specific assumption on the probability distribution of the random variable that models the uncertainty. The developed procedure is demonstrated for a pneumatic drive system.

Fuzzy modelling of an experimental flexible arm

2002 ◽  
Author(s):  
A. Cuce' ◽  
G. Grasso ◽  
G. Sortino ◽  
C. Vinci
Keyword(s):  
Fuzzy Modelling ◽  
Flexible Arm

scholarly journals Further results on robust fuzzy dynamic systems with LMI D-stability constraints

2014 ◽  
Vol 24 (4) ◽  
pp. 785-794 ◽  
Author(s):  
Wudhichai Assawinchaichote
Keyword(s):  
Dynamic Systems ◽  
Fuzzy Controller ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Local System ◽  
Linear Matrix ◽  
Fuzzy Modelling ◽  
Ts Fuzzy Model ◽  
Input Noise ◽  
Takagi Sugeno

Abstract This paper examines the problem of designing a robust H∞ fuzzy controller with D-stability constraints for a class of nonlinear dynamic systems which is described by a Takagi-Sugeno (TS) fuzzy model. Fuzzy modelling is a multi-model approach in which simple sub-models are combined to determine the global behavior of the system. Based on a linear matrix inequality (LMI) approach, we develop a robust H∞ fuzzy controller that guarantees (i) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and (ii) the closed-loop poles of each local system to be within a specified stability region. Sufficient conditions for the controller are given in terms of LMIs. Finally, to show the effectiveness of the designed approach, an example is provided to illustrate the use of the proposed methodology.

scholarly journals Coactive neuro-fuzzy modelling for colour recipe prediction

2002 ◽  
Author(s):  
E. Mizutani ◽  
J.-S.R. Jang ◽  
K. Nishio ◽  
H. Takagi ◽  
D.M. Anslander
Keyword(s):  
Fuzzy Modelling ◽  
Neuro Fuzzy
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