securing interpretability of fuzzy models for modeling nonlinear mimo systems using a hybrid of evolutionary algorithms

Authors

mojtaba eftekhari

mahdi eftekhari

maryam majidi

hossein nezamabadi pour

abstract

in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear mimo systems. due to these multiple and conicting objectives, moga is applied to yield a set of candidates as compact, transparent and valid fuzzy models. also, moga is combined with a powerful search algorithm namely dierential evolution (de). in the proposed algorithm, moga performs the task of membership function tuning as well as rule base identi cation simultaneously while de is utilized only for linear parameter identi cation. practical applicability of the proposed algorithm is examined by two nonlinear system modeling prob- lems used in the literature. the results obtained show the eectiveness of the proposed method.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...

full text

Securing Interpretability of Fuzzy Models for Modeling Nonlinear Mimo Systems Using a Hybrid of Evolutionary Algorithms

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of nonlinear system identification, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, securing low-level and high-level int...

full text

the use of appropriate madm model for ranking the vendors of mci equipments using fuzzy approach

abstract nowadays, the science of decision making has been paid to more attention due to the complexity of the problems of suppliers selection. as known, one of the efficient tools in economic and human resources development is the extension of communication networks in developing countries. so, the proper selection of suppliers of tc equipments is of concern very much. in this study, a ...

15 صفحه اول

Novel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem

Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...

full text

Novel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem

Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...

full text

An Algorithm for Multi-Realization of Nonlinear MIMO Systems

This paper presents a theoretical approach to implementation of the “Multi-realization of nonlinear MIMO systems”. This method aims to find state-variable realization for a set of systems, sharing as many parameters as possible. In this paper a special nonlinear multi-realization problem, namely the multi-realization of feedback linearizable nonlinear systems is considered and an algorithm for ...

full text

My Resources

Save resource for easier access later


Journal title:
iranian journal of fuzzy systems

Publisher: university of sistan and baluchestan

ISSN 1735-0654

volume 9

issue 1 2012

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023