Eliciting transparent fuzzy model using differential evolution

Eftekhari, Mohammad; Katebi, S. D.; Karimi, Mahmood; Jahanmiri, A.

doi:10.1016/j.asoc.2007.02.008

Cited by 49 publications

(36 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various methods have been proposed for tuning fuzzy Membership Functions (MFs) [3][4][5][6]. These methods may generally be categorized in two groups: (1) derivative free methods; (2) derivative-based methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter

Eftekhari

Katebi

2008

Applied Mathematical Modelling

Self Cite

View full text Add to dashboard Cite

This paper presents a two stage procedure for building optimal fuzzy model from data for nonlinear dynamical systems. Both stages are embedded into Genetic Algorithm (GA) and in the first stage emphasis is placed on structural optimization by assigning a suitable fitness to each individual member of population in a canonical GA. These individuals represent coded information about the structure of the model (number of antecedents and rules). This information is consequently utilized by subtractive clustering to partition the input space and construct a compact fuzzy rule base. In the second stage, Unscented Filter (UF) is employed for optimization of model parameters, that is, parameters of the input-output Membership Functions (MFs).The proposed hybrid approach exploits the advantages and utilizes the desirable characteristics of all three algorithms for extracting accurate and compact fuzzy models.Case studies are given to illustrate the efficiency of the modeling procedure. Benchmark examples are analyzed and the results are compared with those obtained by Adaptive Nero-fuzzy Inference System (ANFIS). In all cases enhanced performance and superior results are obtained from the proposed procedure.

show abstract

Section: Introductionmentioning

confidence: 99%

“…For this reason, clustering based methods seem to be preferred to the grid partitioning techniques. Subtractive clustering in conjunction with deferential evolution has recently been applied for extracting fuzzy models for nonlinear processes [4].…”

Section: Introductionmentioning

confidence: 99%

Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter

Eftekhari

Katebi

2008

Applied Mathematical Modelling

Self Cite

View full text Add to dashboard Cite

show abstract

“…ANFIS, however, lacks a mechanism to define the number of nodes and layers, which may cause improper behavior. ANFIS has been applied in many control/decision/prediction areas [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution

Khazraee

Jahanmiri

Ghorayshi

2010

Neural Comput & Applic

View full text Add to dashboard Cite

This paper considers the application of the adaptive neuro-fuzzy inference system (ANFIS) instead of the highly nonlinear model of a reactive batch distillation column for optimization. The architecture has been developed for fuzzy modeling that learns information from a data set, in order to compute the membership function and rule base in accordance with the given input-output data. In this work, the differential evolution algorithm has been employed for optimization of operation policy of reactive batch distillation for producing ethyl acetate. In optimization, minimal batch time and high purity of product are considered, and reflux ratio and final batch time are taken as decision parameters. The results show that the reduced model (ANFIS) is able to properly create a robust model of the reactive batch distillation, and CPU use is reduced to 1/18,000 of that of a real mathematical model. The highest yield and mole fraction of ethyl acetate were achieved through the use of the obtained optimization policy.

show abstract

“…FNN combines the advantages of both fuzzy inference systems in processing granular information and uncertainty and neural networks coming with learning abilities by generating a knowledge base without the need for involving human knowledge [8]. Various methods have been proposed for identification of fuzzy "if-then" rules [9,10]. GA has been widely used for eliciting fuzzy models owing to its ability to search for optimal solutions in high-dimensional solution spaces.…”

Section: Introductionmentioning

confidence: 99%

A Design of Dynamically Simultaneous Search GA-based Fuzzy Neural Networks: Comparative Analysis and Interpretation

Park¹,

Kim²,

Oh³

2013

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

show abstract

Eliciting transparent fuzzy model using differential evolution

Cited by 49 publications

References 16 publications

Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter

Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter

Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution

A Design of Dynamically Simultaneous Search GA-based Fuzzy Neural Networks: Comparative Analysis and Interpretation

Contact Info

Product

Resources

About