Fuzzy System ModelsFuzzy system models evolution EvolutionModels evolution, fuzzy system from FuzzyFuzzy rule bases RulebasesRulebases fuzzy to Fuzzy FunctionsFuzzy functions

2012 ◽  
pp. 1274-1288
Author(s):  
I. Burhan Türkşen
Keyword(s):  
Fuzzy System ◽  
System Models ◽  
Rule Bases

scholarly journals Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake

2007 ◽  
pp. 123-157 ◽  
Author(s):  
Martha Carreño ◽  
Omar Cardona ◽  
Alex Barbat
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Fuzzy Rule ◽  
Neuro Fuzzy ◽  
Computational Intelligence Technique ◽  
Artificial Neural ◽  
Fuzzy Assessment ◽  
Rule Bases ◽  
Building Occupancy

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective of assisting nonexpert professionals of building construction to evaluate the damage and safety of buildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-fuzzy system is proposed, based on a special three-layer feedforward artificial neural network and fuzzy rule bases. The inputs to the system are fuzzy sets, taking into account that the damage levels of the structural components are linguistic variables, defined by means of qualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding of how soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.

A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling

2012 ◽  
pp. 499-508
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy System ◽  
Distance Measure ◽  
System Model ◽  
Rule Base ◽  
Optimization Approach ◽  
Fuzzy Data ◽  
System Models ◽  
Fuzzy System Models

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valuate Type-2 fuzzy clustering approach. In this index, all operations are fuzzy and the minimization method is fuzzy ranking with Hamming distance. The proposed Type-2 fuzzy clustering method is used for development of indirect approach to Type-2 fuzzy modeling, where the rules are extracted from clustering fuzzy numbers (Zadeh, 1965). Then, the Type-2 fuzzy system is tuned by an inference algorithm for optimization of the main parameters of Type-2 parametric system. In this case, the parameters are: Schweizer and Sklar t-Norm and s-Norm, a-cut of rule-bases, combination of FATI and FITA inference approaches, and Yager parametric defuzzification. Finally, the proposed Type-2 fuzzy system model is applied in prediction of the steel additives in steelmaking process. It is shown that, the proposed Type-2 fuzzy system model is superior in comparison with multiple regressions and Type-1 fuzzy system model, in terms of the minimization the effect of uncertainty in the rule-base fuzzy system models an error reduction.

scholarly journals The Data-Driven Fuzzy System with Fuzzy Subtractive Clustering for Time Series Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 3357-3362
Keyword(s):  
Fuzzy System ◽  
Fuzzy Rule ◽  
Training Data ◽  
Subtractive Clustering ◽  
A Value ◽  
Testing Data ◽  
System Input ◽  
Input Variables ◽  
Rule Bases ◽  
Takagi Sugeno

The paper aims to identify input variables of fuzzy systems, generate fuzzy rule bases by using the fuzzy subtractive clustering, and apply fuzzy system of Takagi Sugeno to predict rice stocks in Indonesia. The monthly rice procurement dataset in the period January 2000 to March 2017 are divided into training data (January 2000 to March 2016 and testing data (April 2016 to March 2017). The results of identification of the fuzzy system input variables are lags as system input including . The Input-output clustering fuzzy subtractive and selecting optimal groups by using the cluster thigness measures indicator produced 4 fuzzy rules.The fuzzy system performance in the training data has a value of R2 of 0.8582, while the testing data produces an R2 of 0.7513.

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